df = web.DataReader('BABA', 'iex', start, end)
df = df.dropna()
df = df.iloc[:, :4]
# Calculate some technical indicator
# df['MA_10'] = df['close'].rolling(window=10).mean()
# df['Corr'] = df['close'].rolling(window=10).corr(df['MA_10'])
# df['open-close'] = df['open'] - df['close'].shift(1)
df['ADX'] = ta.ADX(np.array(df['high']),
np.array(df['low']),
np.array(df['close']),
timeperiod=14)
df['BOP'] = ta.BOP(np.array(df['open']), np.array(df['high']),
np.array(df['low']), np.array(df['close']))
df['CCI'] = ta.CCI(np.array(df['high']),
np.array(df['low']),
np.array(df['close']),
timeperiod=14)
# List X and y
df = df.dropna()
X = df.iloc[:, :7]
y = np.where((df['close'].shift(-1) > df['close']), 1, -1)
# Run the Logistic Regression
# Taking training set and test set to 7/3
X_train, X_test, y_train, y_test = model_selection.train_test_split(
X, y.ravel(), random_state=7, shuffle=False)
# LogisticRegression
model = lm.LogisticRegression()
def create_features(twii):
import talib
sma = talib.SMA(twii, timeperiod=120)
wma = talib.WMA(twii, timeperiod=120)
mom = talib.MOM(twii, timeperiod=120)
k, d = talib.STOCH(twii,
twii,
twii,
fastk_period=120,
slowk_period=60,
slowd_period=60)
k2, d2 = talib.STOCH(twii,
twii,
twii,
fastk_period=240,
slowk_period=120,
slowd_period=120)
k3, d3 = talib.STOCH(twii,
twii,
twii,
fastk_period=360,
slowk_period=180,
slowd_period=180)
k4, d4 = talib.STOCH(twii,
twii,
twii,
fastk_period=480,
slowk_period=240,
slowd_period=240)
k5, d5 = talib.STOCH(twii,
twii,
twii,
fastk_period=640,
slowk_period=320,
slowd_period=320)
k6, d6 = talib.STOCH(twii,
twii,
twii,
fastk_period=720,
slowk_period=360,
slowd_period=360)
k7, d7 = talib.STOCH(twii,
twii,
twii,
fastk_period=840,
slowk_period=420,
slowd_period=420)
k8, d8 = talib.STOCH(twii,
twii,
twii,
fastk_period=960,
slowk_period=480,
slowd_period=480)
rsi = talib.RSI(twii, timeperiod=120)
rsi2 = talib.RSI(twii, timeperiod=240)
rsi3 = talib.RSI(twii, timeperiod=480)
rsi4 = talib.RSI(twii, timeperiod=640)
rsi5 = talib.RSI(twii, timeperiod=720)
rsi6 = talib.RSI(twii, timeperiod=840)
macd1, macd2, macd3 = talib.MACD(twii,
fastperiod=120,
slowperiod=60,
signalperiod=60)
willr = talib.WILLR(twii, twii, twii, timeperiod=120)
cci = talib.CCI(twii, twii, twii, timeperiod=120)
return pd.DataFrame({
'RSIb': rsi / 50,
'RSIb2': rsi2 / 50,
'RSIb3': rsi3 / 50,
'RSIb4': rsi4 / 50,
'RSIb5': rsi5 / 50,
'RSIb6': rsi6 / 50,
'MOMb': mom - 0,
'KDb': k - d,
'KDb2': k2 - d2,
'KDb3': k3 - d3,
'KDb4': k4 - d4,
'KDb5': k5 - d5,
'KDb6': k6 - d6,
'KDb7': k7 - d7,
'KDb8': k8 - d8,
'a5': (twii.rolling(5).mean() / twii),
'a10': (twii.rolling(10).mean() / twii),
'a20': (twii.rolling(20).mean() / twii),
'a40': (twii.rolling(40).mean() / twii),
'a80': (twii.rolling(80).mean() / twii),
'a160': (twii.rolling(160).mean() / twii),
'a320': (twii.rolling(320).mean() / twii),
'a640': (twii.rolling(640).mean() / twii),
'a720': (twii.rolling(720).mean() / twii),
'a840': (twii.rolling(840).mean() / twii),
'a960': (twii.rolling(960).mean() / twii),
'a1024': (twii.rolling(1024).mean() / twii),
'b1': twii / twii.shift(50),
'b2': twii / twii.shift(100),
'b3': twii / twii.shift(150),
'b4': twii / twii.shift(200),
'b5': twii / twii.shift(250),
'b6': twii / twii.shift(300),
'b7': twii / twii.shift(350),
'LINEARREG_SLOPE0': talib.LINEARREG_SLOPE(twii, 60),
'LINEARREG_SLOPE1': talib.LINEARREG_SLOPE(twii, 120),
'ADXR0': talib.ADXR(twii, twii, twii, 60),
'ADXR1': talib.ADXR(twii, twii, twii, 120),
'ADXR2': talib.ADXR(twii, twii, twii, 240),
'ADXR3': talib.ADXR(twii, twii, twii, 360),
'ADXR4': talib.ADXR(twii, twii, twii, 480),
'ADXR5': talib.ADXR(twii, twii, twii, 640),
})
def CCI(High, Low, Close, timeperiod=14):
cci = pd.DataFrame()
for i in High.columns:
cci[i] = ta.CCI(High[i], Low[i], Close[i], timeperiod)
return cci
def get_cci(close, high, low, period=14, max_period=10):
cci = talib.CCI(np.array(high), np.array(low), np.array(close), period)
return cci[max_period:]
def parse(code,start_date='0'):
df = pd.read_sql('select * from day_k_data where code="'+code+'" order by date asc;',engine)
buy_price = {}
buy_date = {}
sell_price = {}
sell_date = {}
is_buy = {}
rate = {}
profit = {}
profit['based'] = {}
ma5 = talib.MA(df['close'].values,5)
ma10 = talib.MA(df['close'].values,10)
ma20 = talib.MA(df['close'].values,20)
ma30 = talib.MA(df['close'].values,30)
K, D = talib.STOCH(df['high'].values,df['low'].values,df['close'].values, fastk_period=9, slowk_period=3)
J = K * 3 - D * 2
atr = talib.ATR(df['high'].values,df['low'].values,df['close'].values)
natr = talib.NATR(df['high'].values,df['low'].values,df['close'].values)
trange = talib.TRANGE(df['high'].values,df['low'].values,df['close'].values)
cci = talib.CCI(df['high'].values,df['low'].values,df['close'].values,14)
dif, dea, bar = talib.MACDFIX(df['close'].values)
bar = bar * 2
close = df['close']
start_date_open = 0
for idx in range(len(ma10)):
if df.date[idx] < start_date:
continue
ma10_buy = 1
ma10_sell = 0
bar_buy = 0
bar_sell = 0
cci_buy = 1
cci_sell = 0
dif_buy = 1
dif_sell = 0
K_buy = 1
K_sell = 0
close_val = close[idx]
ma10_val = ma10[idx]
ma20_val = ma20[idx]
ma5_val = ma5[idx]
cci_val = cci[idx]
dif_val = dif[idx]
dea_val = dea[idx]
bar_val = bar[idx]
K_val = K[idx]
D_val = D[idx]
J_val = J[idx]
if start_date_open == 0:
start_date_open = close_val
if idx>=1:
bar_val_y = bar[idx-1]
dif_val_y = dif[idx-1]
dea_val_y = dea[idx-1]
cci_val_y = cci[idx-1]
K_val_y = K[idx-1]
if df.date[idx-1] not in profit['based']:
profit['based'][df.date[idx-1]] = 1.0
profit['based'][df.date[idx]] = profit['based'][df.date[idx-1]] * close[idx] / close[idx-1]
else:
bar_val_y = 0
dif_val_y = 0
dea_val_y = 0
cci_val_y = 0
K_val_y = 0
if close_val > ma10_val:
ma10_buy = 1
if bar_val>0 and bar_val>bar_val_y:
bar_buy = 1
print 'bar_buy'
if cci_val>=100 and cci_val>cci_val_y:
cci_buy = 1
if dif_val>dea_val and dif_val>dif_val_y:
dif_buy = 1
if K_val>D_val and K_val>K_val_y:
K_buy = 1
if close_val<ma10_val:
ma10_sell = 0
if bar_val<0 and bar_val<bar_val_y:
bar_sell = 1
if cci_val<=100 and cci_val<cci_val_y:
cci_sell = 0
if dif_val<dea_val and dif_val<dif_val_y:
dif_sell = 0
if K_val<D_val and K_val<K_val_y:
K_sell = 0
s_type = '11111'
if s_type not in buy_price:
buy_price[s_type] = []
buy_date[s_type] = []
sell_price[s_type] = []
sell_date[s_type] = []
is_buy[s_type] = 0
if ma10_buy * bar_buy * cci_buy * dif_buy * K_buy:
if is_buy[s_type] == 0:
is_buy[s_type] = 1
buy_price[s_type].append(close_val)
buy_date[s_type].append(df.date[idx])
continue
if ma10_sell or bar_sell or cci_sell or dif_sell or K_sell:
if is_buy[s_type] == 1:
is_buy[s_type] = 0
sell_price[s_type].append(close_val)
sell_date[s_type].append(df.date[idx])
rate['based'] = close[len(close)-1] * (1 - 0.002) / start_date_open
for s_type in sell_price:
rate[s_type] = 1.0
profit[s_type] = {}
for i in range(len(buy_price[s_type])):
try:
rate[s_type] = rate[s_type] * (sell_price[s_type][i] * (1 - 0.002) / buy_price[s_type][i])
profit[s_type][sell_date[s_type][i]] = rate[s_type]
print s_type,"buy date : %s, buy price : %.2f, sell date : %s, sell price: %.2f" %(buy_date[s_type][i], buy_price[s_type][i], sell_date[s_type][i], sell_price[s_type][i])
except:
rate[s_type] = rate[s_type] * (close[len(close)-1] * (1 - 0.002) / buy_price[s_type][len(buy_price[s_type])-1])
rate = sorted(rate.items(),key=lambda x:x[1],reverse=True)
return rate,profit
def commodity_channel_index(high, low, close, n):
try:
cci_data = talib.CCI(high, low, close, timeperiod=n)
return cci_data
except Exception as e:
raise (e)
def cci(df, n):
real = talib.CCI(df['high'][(-n - 1):],
df['low'][(-n - 1):],
df['close'][(-n - 1):],
timeperiod=n)
df['CCI' + str(n)][-1] = real[-1]
def __get_quotas(self):
#stock_amount = cral_CNstock_order_ana.main()
greeks = GetDf('MOMO',db_file='../stock_option_data.db')
greeks = greeks.greeks
greeks['15IV'] = greeks['15IV'].apply(float)
greeks['24IV'] = greeks['24IV'].apply(float)
#volume = self.__df['volume'].values
close = self.__df['close']
high_prices = self.__df['high'].values
low_prices = self.__df['low'].values
close_prices = close.values
ma5 = talib.MA(close_prices,5)
ma10 = talib.MA(close_prices,10)
ma20 = talib.MA(close_prices,20)
ma30 = talib.MA(close_prices,30)
K, D = talib.STOCH(high_prices,low_prices,close_prices, fastk_period=9, slowk_period=3)
J = K * 3 - D * 2
sar = talib.SAR(high_prices, low_prices, acceleration=0.05, maximum=0.2)
sar = pd.DataFrame(sar-close)
sar.index = self.__df.index
atr = talib.ATR(high_prices,low_prices,close_prices)
natr = talib.NATR(high_prices,low_prices,close_prices)
trange = talib.TRANGE(high_prices,low_prices,close_prices)
cci = talib.CCI(high_prices,low_prices,close_prices,14)
dif, dea, bar = talib.MACDFIX(close_prices)
bar = bar * 2
#ma5_volume = talib.MA(volume,5)
#ma10_volume = talib.MA(volume,10)
#ma20_volume = talib.MA(volume,20)
#ma30_volume = talib.MA(volume,30)
#dif_volume, dea_volume, bar_volume = talib.MACDFIX(volume)
#bar_volume = bar_volume * 2
df_all = self.__df[['close','volume','high','low']]
df_all.insert(0,'ma5',ma5)
df_all.insert(0,'ma10',ma10)
df_all.insert(0,'ma20',ma20)
df_all.insert(0,'ma30',ma30)
df_all.insert(0,'K',K)
df_all.insert(0,'D',D)
df_all.insert(0,'J',J)
df_all.insert(0,'cci',cci)
df_all.insert(0,'bar',bar)
df_all.insert(0,'dif',dif)
df_all.insert(0,'dea',dea)
df_all.insert(0,'sar',sar)
#df_all.insert(0,'ma5_volume',ma5_volume)
#df_all.insert(0,'ma10_volume',ma10_volume)
#df_all.insert(0,'ma20_volume',ma20_volume)
#df_all.insert(0,'ma30_volume',ma30_volume)
#df_all.insert(0,'bar_volume',bar_volume)
#df_all.insert(0,'dif_volume',dif_volume)
#df_all.insert(0,'dea_volume',dea_volume)
#df_all = pd.concat([df_all,stock_amount],axis=1)
df_all = pd.concat([df_all,greeks],axis=1)
df_yesterday = df_all.T
index_c = df_all.index
added = [np.nan] * len(df_all.columns)
df_yesterday.insert(0, len(df_yesterday.columns), added)
df_yesterday = df_yesterday.T
df_yesterday = df_yesterday.drop(df_all.index[len(df_all.index)-1])
df_yesterday.insert(0, 'index_c', index_c)
df_yesterday = df_yesterday.set_index('index_c')
df_dif = df_all - df_yesterday
df_dif_close_plus_one_day = df_dif.copy()
for i in range(len(df_dif_close_plus_one_day['close'])-1):
df_dif_close_plus_one_day['close'][i] = df_dif_close_plus_one_day['close'][i+1]
df_dif_close_plus_one_day['close'][len(df_dif_close_plus_one_day['close'])-1] = np.nan
df_dif = df_dif.dropna(axis=0,how='any')
df_dif_close_plus_one_day = df_dif_close_plus_one_day.dropna(axis=0,how='any')
return df_dif, df_dif_close_plus_one_day,df_all
def test__cci(self):
me = ta_cci(DF_TEST)[-100:]
ta = talib.CCI(DF_TEST["High"], DF_TEST["Low"],
DF_TEST["Close"])[-100:]
np.testing.assert_array_almost_equal(me, ta / 100)
def set_cci(self):
self.cci = talib.CCI(np.array(self.data['High'], dtype='f8'),
np.array(self.data['Low'], dtype='f8'),
np.array(self.data['Close'], dtype='f8'),
timeperiod=14)
def enhance_with_indicators(data):
set = []
OPEN = data[:, Candle.OPEN]
HIGH = data[:, Candle.HIGH]
LOW = data[:, Candle.LOW]
CLOSE = data[:, Candle.CLOSE]
VOLUME = data[:, Candle.VOLUME]
low_high = talib.BBANDS(CLOSE,
timeperiod=14,
nbdevup=2,
nbdevdn=2,
matype=1)
low_high = np.asarray([low_high[0][-1], low_high[2][-1]]).reshape(-1, 1)
low_high_scaler = StandardScaler()
low_high_scaler.fit(low_high)
one = np.asarray([-1, 1]).reshape(-1, 1)
one_scaler = StandardScaler()
one_scaler.fit(one)
hundred = np.asarray([-100, 100]).reshape(-1, 1)
hundred_scaler = StandardScaler()
hundred_scaler.fit(hundred)
thousand = np.asarray([-1000, 1000]).reshape(-1, 1)
thousand_scaler = StandardScaler()
thousand_scaler.fit(thousand)
million = np.asarray([-1000000, 1000000]).reshape(-1, 1)
million_scaler = StandardScaler()
million_scaler.fit(million)
set.append(scale(OPEN, low_high_scaler))
set.append(scale(HIGH, low_high_scaler))
set.append(scale(LOW, low_high_scaler))
set.append(scale(CLOSE, low_high_scaler))
#Bollinger Bands are envelopes plotted at a standard deviation level above and below a simple moving average of the price.
set.append(
scale(
talib.BBANDS(CLOSE, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0),
low_high_scaler)) #121.03399999999903 19719.281591268886
set.append(
scale(
talib.BBANDS(CLOSE, timeperiod=14, nbdevup=2, nbdevdn=2, matype=1),
low_high_scaler))
#The DEMA uses two exponential moving averages (EMAs) to eliminate lag, as some traders view lag as a problem.
set.append(scale(talib.DEMA(CLOSE, timeperiod=10),
low_high_scaler)) #122.0 19573.564771355504
set.append(scale(talib.DEMA(CLOSE, timeperiod=21),
low_high_scaler)) #122.0 19546.76082510694
set.append(scale(talib.DEMA(CLOSE, timeperiod=100),
low_high_scaler)) #123.84710711425136 19578.715808186673
#However, whereas SMA simply calculates an average of price data, EMA applies more weight to data that is more current.
set.append(scale(talib.EMA(CLOSE, timeperiod=10),
low_high_scaler)) #122.0 19499.362560116417
set.append(scale(talib.EMA(CLOSE, timeperiod=21),
low_high_scaler)) #122.0 19433.26416788178
set.append(scale(talib.EMA(CLOSE, timeperiod=100),
low_high_scaler)) #122.11270000000005 19059.124645340504
#The HTTrendline at a specific bar gives the current Hilbert Transform Trendline as instantaneously measured at that
#bar. In its Series form, the Instantaneous Trendline appears much like a Moving Average, but with minimal lag
#compared with the lag normally associated with such averages for equivalent periods.
set.append(scale(talib.HT_TRENDLINE(CLOSE),
low_high_scaler)) #122.0 19471.324
#Kaufman's Adaptive Moving Average (KAMA) is a moving average designed to account for market noise or volatility.
#KAMA will closely follow prices when the price swings are relatively small and the noise is low.
set.append(scale(talib.KAMA(CLOSE, timeperiod=10),
low_high_scaler)) #122.0 19397.611724437047
set.append(scale(talib.KAMA(CLOSE, timeperiod=21),
low_high_scaler)) #122.0 19336.434082122203
set.append(scale(talib.KAMA(CLOSE, timeperiod=100),
low_high_scaler)) #123.61 19301.746826077375
#The MESA adaptive moving average is a trend-following indicator. It adapts to price movements in a very unique way,
#based on the rate of change (ROC), as measured by the Hilbert Transform Discriminator.
set.append(
scale(talib.MAMA((HIGH + LOW) / 2., fastlimit=0.5, slowlimit=0.05),
low_high_scaler)) #121.04112572694972 19494.294994956996
set.append(scale(talib.MIDPOINT(CLOSE, timeperiod=5),
low_high_scaler)) #122.0 19544.95
set.append(scale(talib.MIDPRICE(LOW, HIGH, timeperiod=5),
low_high_scaler)) #122.0 19562.6
#The parabolic SAR indicator, developed by J. Welles Wilder Jr., is used by traders to determine trend direction
# and potential reversals in price.
set.append(
scale(talib.SAR(HIGH, LOW, acceleration=0.02, maximum=0.2),
low_high_scaler)) #122.0 19660.0
#A simple moving average (SMA) is an arithmetic moving average calculated by adding recent prices and then dividing
# that figure by the number of time periods in the calculation average.
set.append(scale(talib.SMA(CLOSE, timeperiod=5),
low_high_scaler)) #122.0 19553.340000000037
set.append(scale(talib.SMA(CLOSE, timeperiod=25),
low_high_scaler)) #122.0 19405.74400000004
set.append(scale(talib.SMA(CLOSE, timeperiod=50),
low_high_scaler)) #122.0 19286.443999999996
#The Triple Exponential Moving Average (T3) developed by Tim Tillson attempts to offer a moving average with better
#smoothing then traditional exponential moving average. It incorporates a smoothing technique which allows it to
#plot curves more gradual than ordinary moving averages and with a smaller lag.
set.append(scale(talib.T3(CLOSE, timeperiod=5, vfactor=0),
low_high_scaler)) #122.0 19498.31237177043
set.append(
scale(talib.T3(CLOSE, timeperiod=10, vfactor=0),
low_high_scaler)) #122.0 19419.991324685387
set.append(
scale(talib.T3(CLOSE, timeperiod=21, vfactor=0),
low_high_scaler)) #122.84310194419339 19306.63501695168
#The triple exponential moving average was designed to smooth price fluctuations, thereby making it easier to
#identify trends without the lag associated with traditional moving averages (MA).
set.append(scale(talib.TEMA(CLOSE, timeperiod=7),
low_high_scaler)) #122.0 19617.222402494965
set.append(scale(talib.TEMA(CLOSE, timeperiod=15),
low_high_scaler)) #122.0 19586.42515855386
#The Triangular Moving Average is basically a double-smoothed Simple Moving Average that gives more weight to the
#middle section of the data interval. The TMA has a significant lag to current prices and is not well-suited to
#fast moving markets.
set.append(scale(talib.TRIMA(CLOSE, timeperiod=5),
low_high_scaler)) #122.0 19567.31111092877
set.append(scale(talib.TRIMA(CLOSE, timeperiod=25),
low_high_scaler)) #122.0 19459.8816568341
set.append(scale(talib.TRIMA(CLOSE, timeperiod=50),
low_high_scaler)) #122.0 19359.257076923175
#The weighted moving average (WMA) is a technical indicator that assigns a greater weighting to the most recent data
#points, and less weighting to data points in the distant past. The WMA is obtained by multiplying each number in
#the data set by a predetermined weight and summing up the resulting values.
set.append(scale(talib.WMA(CLOSE, timeperiod=5),
low_high_scaler)) #122.0 19567.840000466134
set.append(scale(talib.WMA(CLOSE, timeperiod=10),
low_high_scaler)) #122.0 19527.127272724356
set.append(scale(talib.WMA(CLOSE, timeperiod=21),
low_high_scaler)) #122.0 19479.342424127473
set.append(scale(talib.WMA(CLOSE, timeperiod=50),
low_high_scaler)) #122.0 19355.600000135404
set.append(scale(talib.WMA(CLOSE, timeperiod=100),
low_high_scaler)) #122.21647326732675 19265.66566335264
set.append(scale(talib.LINEARREG(CLOSE, timeperiod=14),
low_high_scaler)) # 122.0 19585.157142857144
set.append(
scale(talib.LINEARREG_INTERCEPT(CLOSE, timeperiod=14),
low_high_scaler)) #121.54000000000003 19643.968571428577
set.append(scale(talib.TSF(CLOSE, timeperiod=14),
low_high_scaler)) #122.0 19609.668131868133
#ADX stands for Average Directional Movement Index and can be used to help measure the overall strength of a trend.
#The ADX indicator is an average of expanding price range values.
set.append(
scale(talib.ADX(HIGH, LOW, CLOSE, timeperiod=10),
hundred_scaler)) #0.0 99.99999999999963
set.append(
scale(talib.ADX(HIGH, LOW, CLOSE, timeperiod=14),
hundred_scaler)) #0.0 99.9999999940751
set.append(
scale(talib.ADX(HIGH, LOW, CLOSE, timeperiod=21),
hundred_scaler)) #0.0 99.99998408446837
#ADXR stands for Average Directional Movement Index Rating. ADXR is a component of the Directional Movement System
#developed by Welles Wilder.
set.append(
scale(talib.ADXR(HIGH, LOW, CLOSE, timeperiod=14),
hundred_scaler)) #0.0 99.9999999892742
#The Aroon indicator is a technical indicator that is used to identify trend changes in the price of an asset,
#as well as the strength of that trend. In essence, the indicator measures the time between highs and the time
#between lows over a time period. ... The indicator signals when this is happening, and when it isn't
set.append(scale(talib.AROON(HIGH, LOW), hundred_scaler)) #0.0 100.0
#The Directional Movement Index, or DMI, is an indicator developed by J. ... An optional third line, called
#directional movement (DX) shows the difference between the lines. When +DI is above -DI, there is more upward
#pressure than downward pressure in the price.
set.append(scale(talib.DX(HIGH, LOW, CLOSE, timeperiod=5),
hundred_scaler)) #0.0 100.0
set.append(scale(talib.DX(HIGH, LOW, CLOSE, timeperiod=21),
hundred_scaler)) #0.0 100.0
set.append(scale(talib.DX(HIGH, LOW, CLOSE, timeperiod=50),
hundred_scaler)) #0.0 100.0
set.append(
scale(talib.MFI(HIGH, LOW, CLOSE, VOLUME, timeperiod=14),
hundred_scaler)) #-5.888410733172162e-08 100.00000000707982
set.append(
scale(talib.MFI(HIGH, LOW, CLOSE, VOLUME, timeperiod=26),
hundred_scaler)) #-1.3802451942902055e-09 100.00000001185656
set.append(
scale(talib.MFI(HIGH, LOW, CLOSE, VOLUME, timeperiod=100),
hundred_scaler)) #-5.3901183535126315e-08 100.00000000091877
set.append(
scale(talib.MINUS_DI(HIGH, LOW, CLOSE, timeperiod=14),
hundred_scaler)) #0.0 99.99999996020233
set.append(
scale(talib.PLUS_DI(HIGH, LOW, CLOSE, timeperiod=14),
hundred_scaler)) #0.0 100.0
set.append(scale(talib.RSI(CLOSE, timeperiod=14),
hundred_scaler)) #0.0 100.0
set.append(
scale(
talib.STOCH(HIGH,
LOW,
CLOSE,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0),
hundred_scaler)) #-1.0137076363510762e-12 100.00000000000279
set.append(
scale(
talib.STOCHF(HIGH,
LOW,
CLOSE,
fastk_period=5,
fastd_period=3,
fastd_matype=0),
hundred_scaler)) #-1.0137076363510762e-12 100.0000000000012
set.append(
scale(
talib.STOCHRSI(CLOSE,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0),
hundred_scaler)) #-1.2166564052525548e-12 100.00000000000011
set.append(
scale(
talib.ULTOSC(HIGH,
LOW,
CLOSE,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28),
hundred_scaler)) # 0.0 100.00000032481957
set.append(
scale(talib.CDL3WHITESOLDIERS(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(scale(talib.CDLDOJI(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(
scale(talib.CDLDRAGONFLYDOJI(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(
scale(talib.CDLGRAVESTONEDOJI(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(scale(talib.CDLHAMMER(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(
scale(talib.CDLHOMINGPIGEON(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(
scale(talib.CDLINVERTEDHAMMER(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(
scale(talib.CDLLADDERBOTTOM(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(
scale(talib.CDLLONGLEGGEDDOJI(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(
scale(talib.CDLMATCHINGLOW(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(
scale(talib.CDLMORNINGDOJISTAR(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(
scale(talib.CDLMORNINGSTAR(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(scale(talib.CDLPIERCING(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(
scale(talib.CDLRICKSHAWMAN(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(
scale(talib.CDLSTICKSANDWICH(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(scale(talib.CDLTAKURI(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
set.append(
scale(talib.CDLUNIQUE3RIVER(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #0 100
#Absolute Price Oscillator crossing above zero is considered bullish, while crossing below zero is bearish. A
#positive indicator value indicates an upward movement, while negative readings signal a downward trend.
set.append(
scale(talib.APO(CLOSE, fastperiod=12, slowperiod=26, matype=1),
thousand_scaler)) #-536.1910463572985 443.13971636041424
set.append(scale(VOLUME, thousand_scaler))
#The Commodity Channel Index (CCI) measures the current price level relative to an average price level over a given
#period of time. CCI is relatively high when prices are far above their average.
set.append(scale(talib.CCI(HIGH, LOW, CLOSE),
thousand_scaler)) #-466.66671166042244 466.66673951370416
#Moving average convergence divergence (MACD) is a trend-following momentum indicator that shows the relationship
#between two moving averages of a security’s price.
set.append(
scale(talib.MACD(CLOSE, fastperiod=5, slowperiod=14, signalperiod=5),
thousand_scaler)) # -536.1910463572985 443.13971636041424
set.append(
scale(talib.MACD(CLOSE, fastperiod=12, slowperiod=26, signalperiod=9),
thousand_scaler)) #-536.1910463572985 443.13971636041424
set.append(
scale(talib.MACD(CLOSE, fastperiod=14, slowperiod=50, signalperiod=25),
thousand_scaler)) # -536.1910463572985 443.13971636041424
set.append(scale(talib.ATR(HIGH, LOW, CLOSE),
thousand_scaler)) #0.0 672.1715311610562
set.append(scale(talib.HT_DCPHASE(CLOSE),
hundred_scaler)) #-44.99298332517037 314.99654478107004
set.append(
scale(talib.LINEARREG_SLOPE(CLOSE, timeperiod=14),
hundred_scaler)) #-222.33604395604476 152.37032967033085
set.append(
scale(talib.STDDEV(CLOSE, timeperiod=5, nbdev=1),
thousand_scaler)) #0.0 709.4023698851089
set.append(scale(talib.MINUS_DM(HIGH, LOW, timeperiod=14),
thousand_scaler)) #0.0 2909.3760999618785
set.append(scale(talib.MOM(CLOSE, timeperiod=10),
thousand_scaler)) #-2508.0 1711.2000000000007
set.append(scale(talib.MOM(CLOSE, timeperiod=25),
thousand_scaler)) # -2508.0 1711.2000000000007
set.append(scale(talib.PLUS_DM(HIGH, LOW, timeperiod=14),
thousand_scaler)) #0.0 3697.0008310558483
set.append(scale(talib.ADOSC(HIGH, LOW, CLOSE, VOLUME),
thousand_scaler)) #-1843.4418435977714 1237.4131984846026
set.append(scale(talib.TRANGE(HIGH, LOW, CLOSE),
thousand_scaler)) #0.0 4000.0
set.append(scale(talib.HT_PHASOR(CLOSE),
thousand_scaler)) #-2873.977625168652 3422.2535328187428
#The Balance of Power indicator measures the market strength of buyers against sellers by assessing the ability of
#each side to drive prices to an extreme level. The calculation is: Balance of Power = (Close price – Open price) /
#(High price – Low price) The resulting value can be smoothed by a moving average.
set.append(scale(talib.BOP(OPEN, HIGH, LOW, CLOSE), one_scaler)) #-1.0 1.0
set.append(scale(talib.ROCP(CLOSE, timeperiod=10),
one_scaler)) #-0.30688987999999995 0.46745909457773854
set.append(scale(talib.ROCR(CLOSE, timeperiod=10),
one_scaler)) #0.69311012 1.4674590945777386
set.append(scale(talib.TRIX(CLOSE, timeperiod=30),
one_scaler)) #-0.6044429731575707 0.434667877456385
set.append(scale(talib.HT_SINE(CLOSE),
one_scaler)) #-0.9999999999996187 0.9999999999940317
set.append(scale(talib.HT_TRENDMODE(CLOSE), one_scaler)) #0 1
set.append(
scale(talib.PPO(CLOSE, fastperiod=12, slowperiod=26, matype=0),
hundred_scaler)) #-13.640389725420714 13.383459677599681
set.append(scale(talib.ROC(CLOSE, timeperiod=10),
hundred_scaler)) #-30.688987999999995 46.74590945777386
set.append(scale(talib.NATR(HIGH, LOW, CLOSE),
one_scaler)) #0.0 7.881777549670427
set.append(scale(talib.HT_DCPERIOD(CLOSE),
hundred_scaler)) #6.91050087362864 49.99951053223339
set.append(scale(talib.CORREL(HIGH, LOW, timeperiod=30),
one_scaler)) #-2.4748737341529163 2.2135943621178655
set.append(scale(talib.AD(HIGH, LOW, CLOSE, VOLUME),
million_scaler)) #-3719.2404462314116 199644.25743563366
set.append(scale(talib.OBV(CLOSE, VOLUME),
million_scaler)) #-23849.75116020021 29139.83770172981
set.append(scale(talib.BETA(HIGH, LOW, timeperiod=5),
million_scaler)) #-2971957.111723269 1250567.1172035346
set.append(
scale(talib.VAR(CLOSE, timeperiod=5, nbdev=1),
million_scaler)) #-1.4901161193847656e-07 503251.7223986089
# The Aroon Oscillator is a trend-following indicator that uses aspects of the Aroon Indicator (Aroon Up and Aroon
# Down) to gauge the strength of a current trend and the likelihood that it will continue. Readings above zero
# indicate that an uptrend is present, while readings below zero indicate that a downtrend is present.
set.append(scale(talib.AROONOSC(HIGH, LOW),
hundred_scaler)) # -100.0 100.0
# The Chande Momentum Oscillator (CMO) is a technical momentum indicator developed by Tushar Chande. The CMO
# indicator is created by calculating the difference between the sum of all recent higher closes and the sum of all
# recent lower closes and then dividing the result by the sum of all price movement over a given time period.
set.append(scale(talib.CMO(CLOSE, timeperiod=5),
hundred_scaler)) # -100.0 100.0
set.append(scale(talib.CMO(CLOSE, timeperiod=14),
hundred_scaler)) # -99.99999998652726 100.0
set.append(scale(talib.CMO(CLOSE, timeperiod=25),
hundred_scaler)) # -99.99854211548185 100.0
set.append(scale(talib.CDL3INSIDE(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDL3LINESTRIKE(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(scale(talib.CDL3OUTSIDE(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLABANDONEDBABY(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(scale(talib.CDLBELTHOLD(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLBREAKAWAY(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLCLOSINGMARUBOZU(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLCOUNTERATTACK(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(scale(talib.CDLDOJISTAR(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLENGULFING(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLGAPSIDESIDEWHITE(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(scale(talib.CDLHARAMI(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLHARAMICROSS(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(scale(talib.CDLHIGHWAVE(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(scale(talib.CDLKICKING(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLKICKINGBYLENGTH(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(scale(talib.CDLLONGLINE(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(scale(talib.CDLMARUBOZU(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLRISEFALL3METHODS(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLSEPARATINGLINES(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLSHORTLINE(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLSPINNINGTOP(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLTASUKIGAP(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(scale(talib.CDLTRISTAR(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.CDLXSIDEGAP3METHODS(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 100
set.append(
scale(talib.LINEARREG_ANGLE(CLOSE, timeperiod=14),
hundred_scaler)) #-89.74230272272693 89.62397563202859
set.append(
scale(talib.WILLR(HIGH, LOW, CLOSE, timeperiod=14),
hundred_scaler)) # -100.00000000000001 0.0
#The Two Crows is a three-line bearish reversal candlestick pattern. The pattern requires confirmation, that is,
#the following candles should break a trendline or the nearest support area which may be formed by the first
#candle's line. If the pattern is not confirmed it may act only as a temporary pause within an uptrend.
set.append(scale(talib.CDL2CROWS(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 0
set.append(
scale(talib.CDL3BLACKCROWS(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 0
set.append(
scale(talib.CDLADVANCEBLOCK(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) # -100 0
set.append(
scale(talib.CDLDARKCLOUDCOVER(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 0
set.append(
scale(talib.CDLEVENINGDOJISTAR(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 0
set.append(
scale(talib.CDLEVENINGSTAR(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 0
set.append(
scale(talib.CDLHANGINGMAN(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 0
set.append(
scale(talib.CDLIDENTICAL3CROWS(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 0
set.append(scale(talib.CDLINNECK(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 0
set.append(scale(talib.CDLONNECK(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 0
set.append(
scale(talib.CDLSHOOTINGSTAR(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 0
set.append(
scale(talib.CDLSTALLEDPATTERN(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 0
set.append(
scale(talib.CDLTHRUSTING(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-100 0
set.append(scale(talib.CDLHIKKAKE(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-200 200
set.append(
scale(talib.CDLHIKKAKEMOD(OPEN, HIGH, LOW, CLOSE),
hundred_scaler)) #-200 200
return np.concatenate(set)
def generate_tech_data(stock,
open_name,
close_name,
high_name,
low_name,
max_time_window=10):
open_price = stock[open_name].values
close_price = stock[close_name].values
low_price = stock[low_name].values
high_price = stock[high_name].values
data = stock.copy()
data['MOM'] = talib.MOM(close_price, timeperiod=max_time_window)
data['HT_DCPERIOD'] = talib.HT_DCPERIOD(close_price)
data['HT_DCPHASE'] = talib.HT_DCPHASE(close_price)
data['sine'], data['leadsine'] = talib.HT_SINE(close_price)
data['inphase'], data['quadrature'] = talib.HT_PHASOR(close_price)
data['ADXR'] = talib.ADXR(high_price,
low_price,
close_price,
timeperiod=max_time_window)
data['APO'] = talib.APO(close_price,
fastperiod=max_time_window // 2,
slowperiod=max_time_window)
data['AROON_UP'], _ = talib.AROON(high_price,
low_price,
timeperiod=max_time_window)
data['CCI'] = talib.CCI(high_price,
low_price,
close_price,
timeperiod=max_time_window)
data['PLUS_DI'] = talib.PLUS_DI(high_price,
low_price,
close_price,
timeperiod=max_time_window)
data['PPO'] = talib.PPO(close_price,
fastperiod=max_time_window // 2,
slowperiod=max_time_window)
data['macd'], data['macd_sig'], data['macd_hist'] = talib.MACD(
close_price,
fastperiod=max_time_window // 2,
slowperiod=max_time_window,
signalperiod=max_time_window // 2)
data['CMO'] = talib.CMO(close_price, timeperiod=max_time_window)
data['ROCP'] = talib.ROCP(close_price, timeperiod=max_time_window)
data['fastk'], data['fastd'] = talib.STOCHF(high_price, low_price,
close_price)
data['TRIX'] = talib.TRIX(close_price, timeperiod=max_time_window)
data['ULTOSC'] = talib.ULTOSC(high_price,
low_price,
close_price,
timeperiod1=max_time_window // 2,
timeperiod2=max_time_window,
timeperiod3=max_time_window * 2)
data['WILLR'] = talib.WILLR(high_price,
low_price,
close_price,
timeperiod=max_time_window)
data['NATR'] = talib.NATR(high_price,
low_price,
close_price,
timeperiod=max_time_window)
data = data.drop([open_name, close_name, high_name, low_name], axis=1)
data = data.dropna().astype(np.float32)
return data
df['CDLSEPARATINGLINES'] = talib.CDLSEPARATINGLINES(open, high, low, close)
df['CDLSHOOTINGSTAR'] = talib.CDLSHOOTINGSTAR(open, high, low, close)
df['CDLSHORTLINE'] = talib.CDLSHORTLINE(open, high, low, close)
df['CDLSPINNINGTOP'] = talib.CDLSPINNINGTOP(open, high, low, close)
df['CDLSTALLEDPATTERN'] = talib.CDLSTALLEDPATTERN(open, high, low, close)
df['CDLUPSIDEGAP2CROWS'] = talib.CDLUPSIDEGAP2CROWS(open, high, low, close)
df['CDLXSIDEGAP3METHODS'] = talib.CDLXSIDEGAP3METHODS(open, high, low, close)
df['CDLUNIQUE3RIVER'] = talib.CDLUNIQUE3RIVER(open, high, low, close)
df['ADOSC'] = talib.ADOSC(high,
low,
close,
volume,
fastperiod=3,
slowperiod=10)
df['OBV'] = talib.OBV(close, volume)
df['CCI'] = talib.CCI(high, low, close, timeperiod=14)
df['ROC'] = talib.ROC(close, timeperiod=10)
df['ROCR'] = talib.ROCR(close, timeperiod=10)
df['HT_DCPERIOD'] = talib.HT_DCPERIOD(close)
df['ADXR'] = talib.ADXR(np.array(high),
np.array(low),
np.array(close),
timeperiod=14)
df['BOP'] = talib.BOP(np.array(open), np.array(high), np.array(low),
np.array(close))
df['CMO'] = talib.CMO(np.array(close), timeperiod=14)
df['DX'] = talib.DX(np.array(high),
np.array(low),
np.array(close),
timeperiod=14) #21
df['MFI'] = talib.MFI(np.array(high),
def technical_indicators_panda_dataframe(df_price):
open = df_price['Open']
high = df_price['High']
low = df_price['Low']
close = df_price['Close']
volume = df_price['Volume']
## Commoditty Channel Index
cci = tb.CCI(high, low, close, timeperiod=20)
## Rate of change
roc = tb.ROC(close, timeperiod=21)
## Momentum Indicators Functions
#Aroon
aroondown, aroonup = tb.AROON(high, low, timeperiod=14)
#Average Directional Movement Index
adx = tb.ADX(high, low, close, timeperiod=14)
di_plus = tb.PLUS_DI(high, low, close, timeperiod=14)
di_minus = tb.MINUS_DI(high, low, close, timeperiod=14)
#Money Flow Index
mfi = tb.MFI(high, low, close, volume, timeperiod=14)
#Relative Strength Index
rsi = tb.RSI(close, timeperiod=14)
#Stochastic
slowk, slowd = tb.STOCH(high,
low,
close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
#STOCHF - Stochastic Fast
fastk, fastd = tb.STOCHF(high,
low,
close,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
#Moving Average Convergence/Divergence
macd, macdema, macdhist = tb.MACD(close,
fastperiod=12,
slowperiod=26,
signalperiod=9)
## Overlap Studies Functions
#Bollinger Bands
upperband, middleband, lowerband = tb.BBANDS(close,
timeperiod=5,
nbdevup=2,
nbdevdn=2,
matype=0)
#Weighted Moving Average
wma5 = tb.WMA(close, timeperiod=5)
wma30 = tb.WMA(close, timeperiod=30)
#Exponential Moving Average
ema5 = tb.EMA(close, timeperiod=5)
ema21 = tb.EMA(close, timeperiod=21)
#Double Exponential Moving Average
dema5 = tb.DEMA(close, timeperiod=5)
dema21 = tb.DEMA(close, timeperiod=21)
#Triple Exponential Moving Average
tema = tb.TEMA(close, timeperiod=30)
#Triangular Moving Average
trima5 = tb.DEMA(close, timeperiod=5)
trima30 = tb.TRIMA(close, timeperiod=30)
## Volume indicators Functions
#AD - Chaikin A/D Line
chaikin = tb.AD(high, low, close, volume)
##=> MERGE
kwargs = {"cci":cci, "roc": roc, "aroondown": aroondown, "aroonup": aroonup, "adx": adx, "di_plus":di_plus, "di_minus":di_minus, "mfi": mfi, "rsi": rsi, "slowk": slowk, "slowd": slowd, "fastk": fastk, "fastd": fastd, "macd": macd, "macdema": macdema, "macdhist": macdhist,\
"upperband": upperband , "middleband": middleband , "lowerband": lowerband, "wma5" : wma5, "wma30" : wma30, "ema5" : ema5, "ema21" : ema21, "dema21": dema21, "dema5": dema5, "tema": tema, "trima5": trima5,"trima30": trima30,\
"chaikin": chaikin}
return df_price.assign(**kwargs)
def indicator(filename):
database = pd.read_csv(filename)
close = np.array(database.close)
high = np.array(database.high)
low = np.array(database.low)
volume = np.array(database.volume)
o = np.array(database.open)
#简单区分其到底处于什么区间内
Add('OPEN', o)
Add('HIGH', high)
Add('LOW', low)
Add('CLOSE', close)
Add('VOLUME', volume)
upperband, middleband, lowerband = talib.BBANDS(close,
timeperiod=20,
nbdevup=2,
nbdevdn=2,
matype=0)
Length = len(upperband)
increase = []
for i in range(0, Length):
if math.isnan(upperband[i]):
increase.append(np.nan)
else:
increase.append(upperband[i] - middleband[i])
Add('BBANDS', np.asarray(increase))
real = talib.DEMA(close, timeperiod=10)
real1 = talib.DEMA(close, timeperiod=20)
real0 = []
for i in range(0, Length):
if not (math.isnan(real[i]) or math.isnan(real1[i])):
real0.append(real[i] - real1[i])
else:
real0.append(np.nan)
Add('DEMA', real0)
real = talib.EMA(close, timeperiod=5)
real1 = talib.EMA(close, timeperiod=10)
real0 = []
for i in range(0, Length):
if not (math.isnan(real[i]) or math.isnan(real1[i])):
real0.append(real[i] - real1[i])
else:
real0.append(np.nan)
Add('EMA', real0)
#暂时不会用这个指标
real = talib.HT_TRENDLINE(close)
Add('HT_TRENDLINE', real)
real = talib.KAMA(close, timeperiod=30)
real1 = talib.KAMA(close, timeperiod=60)
real0 = []
for i in range(0, Length):
if not (math.isnan(real[i]) or math.isnan(real1[i])):
real0.append(real[i] - real1[i])
else:
real0.append(np.nan)
Add('KAMA', real0)
real = talib.MA(close, timeperiod=7, matype=0)
real1 = talib.MA(close, timeperiod=14, matype=0)
real0 = []
for i in range(Length):
if not (math.isnan(real[i]) or math.isnan(real1[i])):
real0.append(real[i] - real1[i])
else:
real0.append(np.nan)
Add('MA', real0)
#暂时没找到怎么去用
mama, fama = talib.MAMA(close, fastlimit=0.5, slowlimit=0.05)
real0 = []
for i in range(0, Length):
if not (math.isnan(real[i]) or math.isnan(real1[i])):
real0.append(mama[i] - fama[i])
else:
real0.append(np.nan)
Add('MAMA', np.asarray(real0))
#没找到
real = talib.MIDPOINT(close, timeperiod=14)
Add('MIDPOINT', real)
#没找到
real = talib.MIDPRICE(high, low, timeperiod=14)
Add('MIDPRICE', real)
real = talib.SAR(high, low, acceleration=0, maximum=0)
real0 = []
for i in range(0, Length):
if not math.isnan(real[i]):
real0.append(close[i] - real[i])
else:
real0.append(np.nan)
Add('SAR', real0)
#暂时不会
real = talib.SAREXT(high,
low,
startvalue=0,
offsetonreverse=0,
accelerationinitlong=0,
accelerationlong=0,
accelerationmaxlong=0,
accelerationinitshort=0,
accelerationshort=0,
accelerationmaxshort=0)
Add('SAREXT', real)
real = talib.SMA(close, timeperiod=3)
real1 = talib.SMA(close, timeperiod=5)
real0 = []
for i in range(0, Length):
if not (math.isnan(real[i]) or math.isnan(real1[i])):
real0.append(real[i] - real1[i])
else:
real0.append(np.nan)
Add('SMA', real0)
#暂时不懂
real = talib.T3(close, timeperiod=5, vfactor=0)
Add('T3', real)
real = talib.TEMA(close, timeperiod=7)
real1 = talib.TEMA(close, timeperiod=14)
real0 = []
for i in range(0, Length):
if not (math.isnan(real[i]) or math.isnan(real1[i])):
real0.append(real[i] - real1[i])
else:
real0.append(np.nan)
Add('TEMA', real0)
real = talib.TRIMA(close, timeperiod=7)
real1 = talib.TRIMA(close, timeperiod=14)
real0 = []
for i in range(0, Length):
if not (math.isnan(real[i]) or math.isnan(real1[i])):
real0.append(real[i] - real1[i])
else:
real0.append(np.nan)
Add('TRIMA', real0)
real = talib.WMA(close, timeperiod=7)
real1 = talib.WMA(close, timeperiod=14)
real0 = []
for i in range(0, Length):
if not (math.isnan(real[i]) or math.isnan(real1[i])):
real0.append(real[i] - real1[i])
else:
real0.append(np.nan)
Add('WMA', real0)
#ADX与ADXR的关系需要注意一下
real = talib.ADX(high, low, close, timeperiod=14)
Add('ADX', real)
real = talib.ADXR(high, low, close, timeperiod=14)
Add('ADXR', real)
#12个和26个简单移动平均线的差值
real = talib.APO(close, fastperiod=12, slowperiod=26, matype=0)
Add('APO', real)
'''
aroondown, aroonup = talib.AROON(high, low, timeperiod=14)
real0 = []
for i in range(0,Length):
if not(math.isnan(aroondown) or math.isnan(aroonup)):
real0.append(aroonup[i] - aroondown[i])
else:
real0.append(numpy.nan)
Add('AROON',numpy.asarray(real0))
'''
#AROONOSC就是Aroonup-aroondown
real = talib.AROONOSC(high, low, timeperiod=14)
Add('AROONOSC', real)
#不懂
real = talib.BOP(o, high, low, close)
Add('BOP', real)
#
real = talib.CCI(high, low, close, timeperiod=14)
Add('CCI', real)
real = talib.CMO(close, timeperiod=14)
Add('CMO', real)
#需要再考虑一下，因为DX代表的市场的活跃度
real = talib.DX(high, low, close, timeperiod=14)
Add('DX', real)
macd, macdsignal, macdhist = talib.MACD(close,
fastperiod=12,
slowperiod=26,
signalperiod=9)
Add('MACD', macdhist)
macd, macdsignal, macdhist = talib.MACDEXT(close,
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0)
Add('MACDEXT', macdhist)
macd, macdsignal, macdhist = talib.MACDFIX(close, signalperiod=9)
Add('MACDFIX', macdhist)
real = talib.MFI(high, low, close, volume, timeperiod=14)
real1 = talib.MA(real, 7)
real0 = []
for i in range(0, Length):
if not (math.isnan(real[i]) or math.isnan(real1[i])):
real0.append(real[i] - real1[i])
else:
real0.append(np.nan)
Add('MFI', np.asarray(real0))
real = talib.MINUS_DI(high, low, close, timeperiod=14)
real1 = talib.PLUS_DI(high, low, close, timeperiod=14)
real0 = []
for i in range(0, Length):
if not (math.isnan(real[i]) or math.isnan(real1[i])):
real0.append(real1[i] - real[i])
else:
real0.append(np.nan)
Add('PLUS_DI', np.asarray(real0))
real = talib.MINUS_DM(high, low, timeperiod=14)
Add('MINUS_DM', real)
#虽然大概了解了规则，但在标普500上怎么用还不是很清楚
real = talib.MOM(close, timeperiod=14)
Add('MOM', real)
real = talib.PLUS_DM(high, low, timeperiod=14)
Add('PLUS_DM', real)
#暂时不用
real = talib.PPO(close, fastperiod=12, slowperiod=26, matype=0)
Add('PPO', real)
real = talib.ROC(close, timeperiod=14)
Add('ROC', real)
real = talib.ROCP(close, timeperiod=14)
Add('ROCP', real)
real = talib.ROCR(close, timeperiod=14)
Add('ROCR', real)
real = talib.ROCR100(close, timeperiod=14)
Add('ROCR100', real)
real = talib.RSI(close, timeperiod=14)
Add('RSI', real)
slowk, slowd = talib.STOCH(high,
low,
close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
slowj = []
for i in range(Length):
if not (math.isnan(slowk[i]) or math.isnan(slowd[i])):
slowj.append(3 * slowk[i] - 2 * slowd[i])
else:
slowj.append(np.nan)
Add('STOCH', np.asarray(slowj))
fastk, fastd = talib.STOCHF(high,
low,
close,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
fastj = []
for i in range(Length):
if not (math.isnan(fastk[i]) or math.isnan(fastd[i])):
fastj.append(3 * fastk[i] - 2 * fastd[i])
else:
fastj.append(np.nan)
Add('STOCHF', np.asarray(fastj))
fastk, fastd = talib.STOCHRSI(close,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
fastj = []
for i in range(Length):
if not (math.isnan(fastk[i]) or math.isnan(fastd[i])):
fastj.append(3 * fastk[i] - 2 * fastd[i])
else:
fastj.append(np.nan)
Add('STOCHRSI', np.asarray(fastj))
real = talib.TRIX(close, timeperiod=30)
real1 = talib.MA(real, 6)
real0 = []
for i in range(0, Length):
if not (math.isnan(real[i] or math.isnan(real1[i]))):
real0.append(real[i] - real1[i])
else:
real0.append(np.nan)
Add('TRIX', real)
real = talib.ULTOSC(high,
low,
close,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
Add('ULTOSC', real)
real = talib.WILLR(high, low, close, timeperiod=14)
real0 = []
for i in range(0, Length):
if not math.isnan(real[i]):
if real[i] > -20:
real0.append(1.0)
elif real[i] < -80:
real0.append(-1.0)
else:
real0.append(0.0)
else:
real0.append(np.nan)
Add('WILLR', np.asarray(real0))
real = talib.AD(high, low, close, volume)
real1 = talib.MA(real, 6)
real0 = []
for i in range(0, Length):
if not (math.isnan(real[i]) or math.isnan(real1[i])):
real0.append(real[i] - real1[i])
else:
real0.append(np.nan)
Add('AD', np.asarray(real0))
real = talib.ADOSC(high, low, close, volume, fastperiod=3, slowperiod=10)
Add('ADOSC', real)
#对于每个指标的处理还是很有问题的呀
real = talib.OBV(close, volume)
Add('OBV', real)
real = talib.ATR(high, low, close, timeperiod=14)
Add('ATR', real)
real = talib.NATR(high, low, close, timeperiod=14)
Add('NATR', real)
real = talib.TRANGE(high, low, close)
Add('TRANGE', real)
integer = talib.HT_TRENDMODE(close)
Add('HT_TRENDMODE', integer)
real = talib.LINEARREG_SLOPE(close, timeperiod=14)
Add('LINEARREG_SLOPE', real)
real = talib.STDDEV(close, timeperiod=5, nbdev=1)
Add('STDDEV', real)
real = talib.TSF(close, timeperiod=14)
Add('TSF', real)
real = talib.VAR(close, timeperiod=5, nbdev=1)
Add('VAR', real)
real = talib.MEDPRICE(high, low)
Add('MEDPRICE', real)
real = talib.TYPPRICE(high, low, close)
Add('TYPPRICE', real)
real = talib.WCLPRICE(high, low, close)
Add('WCLPRICE', real)
real = talib.DIV(high, low)
Add('DIV', real)
real = talib.MAX(close, timeperiod=30)
Add('MAX', real)
real = talib.MIN(close, timeperiod=30)
Add('MIN', real)
real = talib.SUB(high, low)
Add('SUB', real)
real = talib.SUM(close, timeperiod=30)
Add('SUM', real)
return [dict1, dict2]
def CCI(high, low, close, timeperiod=14):
# COMMODITY CHANNEL INDEX
real_ = ta.CCI(high, low, close, timeperiod)
return real_
def getIndi(tiker='BTC'):
df = pd.read_csv("csv/" + tiker + "_ohlcv.csv")
cci_ = talib.CCI(df.high, df.low, df.close, 14)
cci_signal = talib.MA(cci_, 9, 0)
mfi_ = talib.MFI(df.high, df.low, df.close, df.volume, 14)
obv_ = talib.OBV(df.close, df.volume)
obv_signal = talib.MA(obv_, 9, 0)
adx_ = talib.ADX(df.high, df.low, df.close, 14)
di_pl = talib.PLUS_DI(df.high, df.low, df.close, 14)
di_mi = talib.MINUS_DI(df.high, df.low, df.close, 14)
sar_ = talib.SAR(df.high, df.low, 0.02, 0.2)
upperband, middleband, lowerband = talib.BBANDS(df.close, 7, 2, 2, 0)
bandwidth = 100.0 * (upperband - lowerband) / middleband
df['band'] = 0.0
for i in range(0, len(bandwidth.index)):
if bandwidth[i] < 5:
df['band'][i] = 0.0
continue
df['band'][i] = bandwidth[i]
df['cci'] = 0
chk = 0
for i in range(1, len(cci_.index)):
if cci_[i] == cci_[i] and cci_signal[i] == cci_signal[i]:
if cci_[i - 1] <= cci_signal[i - 1] and cci_[i] >= cci_signal[i]:
if cci_[i] <= -100:
chk = 8
elif cci_[i] <= 0:
chk = 7
elif cci_[i] <= 100:
chk = 6
else:
chk = 5
elif cci_[i - 1] >= cci_signal[i - 1] and cci_[i] <= cci_signal[i]:
if cci_[i] >= 100:
chk = 1
elif cci_[i] >= 0:
chk = 2
elif cci_[i] >= -100:
chk = 3
else:
chk = 4
df['cci'][i] = chk
df['adx'] = 0
chk1 = 0
chk2 = 0
chk3 = 0
chk4 = 0
chk5 = 0
chk6 = 0
max_ = 0
min_ = 0
for i in range(1, len(df.index)):
if di_pl[i] == di_pl[i] and di_mi[i] == di_mi[i] and adx_[i] == adx_[
i] and sar_[i] == sar_[i]:
if adx_[i] >= 20:
if sar_[i] >= df['close'][i]:
chk1 = 0
else:
chk1 = 1
else:
chk1 = 2
if di_pl[i - 1] < di_mi[i - 1] and di_pl[i] > di_mi[i]:
chk2 = 1
chk5 = 0
chk6 = 0
if chk3 == 0:
max_ = i
elif df['close'][i] >= df['high'][max_]:
chk4 = 1
chk3 = 1
elif di_pl[i - 1] > di_mi[i - 1] and di_pl[i] < di_mi[i]:
chk2 = 2
chk3 = 0
chk4 = 0
if chk5 == 0:
min_ = i
elif df['close'][i] <= df['low'][min_]:
chk6 = 1
chk5 = 1
if chk1 == 0:
if chk2 == 1:
if chk4 == 1:
df['adx'][i] = 12
else:
df['adx'][i] = 11
else:
if chk6 == 1:
df['adx'][i] = 9
else:
df['adx'][i] = 10
elif chk1 == 1:
if chk2 == 1:
if chk4 == 1:
df['adx'][i] = 4
else:
df['adx'][i] = 3
else:
if chk6 == 1:
df['adx'][i] = 1
else:
df['adx'][i] = 2
else:
if chk2 == 1:
if chk4 == 1:
df['adx'][i] = 8
else:
df['adx'][i] = 7
else:
if chk6 == 1:
df['adx'][i] = 5
else:
df['adx'][i] = 6
df['obv'] = 0
for i in range(len(df.index)):
if obv_[i] == obv_[i] and obv_signal[i] == obv_signal[i]:
df['obv'][i] = obv_[i] - obv_signal[i]
df = df.drop(df.columns[[0, 1, 2, 3, 4]], axis=1)
df.to_csv("csv/" + tiker + "_indi1.csv", index=False)
return df
def cci(self, n, array=False):
"""CCI指标"""
result = talib.CCI(self.high, self.low, self.close, n)
if array:
return result
return result[-1]
def crawStock(dayNum):
# ============================== 创建汇总表模板 ==============================
# ============= 取出日期后缀 =============
demo1 = ts.pro_bar('600000.SH', adj='qfq', start_date=start_time2)
demo = demo1.sort_values('trade_date', ascending=True) # sort
demo.index = demo1.index # sort the index
demoT = demo['trade_date'].T # get column names
demoT = demoT[-dayNum:] # slice
# print(demoT)
# ============= 0 收盘率、收盘价和最高价 =============
CloseRatio = ["%CloseRatio_" + str(demoT.iloc[-1])]
closeName = demoT.tolist() # df -> list
closeName = ['Close_day_' + i for i in closeName] # add index label
highName = demoT.tolist() # df -> list
highName = ['High_day_' + i for i in highName] # add index label
# ============= 1 日前复权数据 DB2 =============
dateName = demoT.tolist() # df -> list
dateName = ['DB2_day_' + i for i in dateName] # add index label
# ============= 2 周前复权数据 DB2 =============
demo_week1 = ts.pro_bar('600000.SH',
freq='W',
adj='qfq',
start_date=start_time2)
demo_week = demo_week1.sort_values('trade_date', ascending=True)
demo_week.index = demo_week1.index
demo_weekT = demo_week['trade_date'].T
demo_weekT = demo_weekT[-dayNum:]
weekName = demo_weekT.tolist()
weekName = ['DB2_week_' + i for i in weekName]
# ============= 3 MACD_hist =============
macdName = demoT.tolist() # df -> list
macdName = ['MACD_' + i for i in macdName] # add index label
# ============= 4 RSI =============
RSIName = demoT.tolist() # df -> list
RSIName = ['RSI_' + i for i in RSIName] # add index label
# ============= 5 CCI =============
CCIName = demoT.tolist() # df -> list
CCIName = ['CCI_' + i for i in CCIName] # add index label
# ============= 6 KDJ =============
KtName = demoT.tolist() # df -> list
KtName = ['Kt_' + i for i in KtName] # add index label
DtName = demoT.tolist() # df -> list
DtName = ['Dt_' + i for i in DtName] # add index label
JtName = demoT.tolist() # df -> list
JtName = ['Jt_' + i for i in JtName] # add index label
# ============= 7 企业基本数据 =============
empl = pro.stock_company()
emplName = empl.columns.tolist()
# demo_empl = empl[empl['ts_code'].isin(["002635.SZ"])]
# print(demo_empl)
# demo_empl.to_csv('./emp_info.csv', encoding="utf_8_sig", float_format='%.2f')
# ============= 汇总文件前缀 =============
colsName = CloseRatio + closeName + highName + dateName + weekName \
+ macdName + RSIName + CCIName \
+ KtName + DtName + JtName \
+ emplName
conclude = pd.DataFrame(columns=colsName)
print(conclude)
concLen = len(conclude.columns.tolist())
# ============= 8 检测汇总表文件是否存在 =============
saveFile = savePath + "/conclusion_weighted_" + str(dayNum) + ".csv"
if os.path.exists(saveFile):
print("文件", saveFile, "已存在，开始续写。")
else:
conclude.to_csv(saveFile, encoding="utf_8_sig", float_format='%.2f')
# ============= 9 记录已汇总股票指数 =============
conc = pd.read_csv(saveFile, error_bad_lines=False, engine='python')
tlist = np.array(conc.iloc[:, 0].values).tolist()
# print(tlist)
# ============================== 开始爬取数据 ==============================
for sheet in sheets:
# ============= 处理工作表 =============
# 0-1 读入单张工作表
print("正在读取第", sheet, "张工作表")
try:
sheet_i = pd.read_excel(path, sheet_name=sheet)
except:
print("无法找到工作表: ", sheet, "，已跳过")
continue
# 0-2读入股票代码列
stockCode = sheet_i["代码"]
# ============= 0-3 根据股票代码读取日指数并保存 =============
for SC in stockCode:
code = str(SC)
# ============= 1 补零到六位代码 =============
while len(code) < 6:
code = "0" + code
# 2 补全复权代码
if code[0] == '6':
code = code + '.SH'
else:
code = code + '.SZ'
# 2-2 跳过已汇总代码
if code in tlist:
print("代码", code, "已在表中，跳过读取。")
continue
# ============= 3 读入企业信息 =============
try:
# df_basics = empl.loc[code]
df_basics = empl[empl['ts_code'].isin([code])].iloc[0]
# print(df_basics)
# print(type(df_basics))
except:
print("无法找到企业代码: ", code, " 的信息")
continue
# print(df_basics)
# 4 读入日复权指数
df1 = ts.pro_bar(code, adj='qfq', start_date='2017-01-01')
# df1.to_csv('./df.csv', encoding="utf_8_sig", float_format='%.2f')
# 4-1 如果数据为空
if df1 is None:
print("股票代码", end='')
print(code, end='')
print("的查询结果为空")
continue
# 4-2 数据量小于21
if len(df1) < 21:
print("股票代码", end='')
print(code, end='')
print("的记录数量小于21，不予计算 DB2")
continue
# 5-1 计算 DB1 和 DB2
df = df1.sort_values('trade_date', ascending=True)
df.index = df1.index
try:
df['MIN_21'], df['MAX_21'], df['DB1'] = calculateDB1(
df.high, df.low, df.close, N, N2, [])
df['DB2'] = calculateDB2(df['DB1'], N, N2, [])
except:
print("Error code is: ", code)
continue
# 5-2 计算 MACD
try:
df["MACD_DIFF"], df["MACD_DEA"], df["MACD_macdhist"] = \
talib.MACD(df.close, fastperiod=12, slowperiod=26, signalperiod=9)
except:
print("MACD Error code is: ", code)
continue
# 5-3 计算 RSI
try:
df['RSI'] = talib.RSI(df.open, timeperiod=12)
except:
print("RSI Error code is: ", code)
continue
# 5-4 计算 CCI
try:
df["CCI"] = talib.CCI(df.high, df.low, df.close, timeperiod=14)
except:
print("CCI Error code is: ", code)
continue
# 5-5 计算 KDJ
try:
df['Kt'], df['Dt'] = talib.STOCH(df.high,
df.low,
df.close,
fastk_period=9,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
df['Jt'] = list(
map(lambda x, y: 3 * x - 2 * y, df['Kt'], df['Dt']))
except:
print("KDJ Error code is: ", code)
continue
# 5-6 取出指标作为 series 并取出五天内数据
df.index = df['trade_date']
Ratio = (df['close'][-1] -
df['close'][-dayNum:].min()) / df['close'][-1] * 100
closeR = pd.Series([Ratio])
# print(type(closeR))
closeR.index = ["%CloseRatio_" + str(df['trade_date'][-1])]
closeRow = df['close'][-dayNum:]
closeRow.index = df['trade_date'][-dayNum:].map(
lambda x: "Close_day_" + str(x)).tolist()
highRow = df['high'][-dayNum:]
highRow.index = df['trade_date'][-dayNum:].map(
lambda x: "High_day_" + str(x)).tolist()
newRow = df['DB2'][-dayNum:]
newRow.index = df['trade_date'][-dayNum:].map(
lambda x: "DB2_day_" + str(x)).tolist()
# print(type(newRow))
MACD_Row = df["MACD_macdhist"][-dayNum:]
MACD_Row.index = df['trade_date'][-dayNum:].map(
lambda x: "MACD_" + str(x)).tolist()
RSI_Row = df['RSI'][-dayNum:]
RSI_Row.index = df['trade_date'][-dayNum:].map(
lambda x: "RSI_" + str(x)).tolist()
CCI_Row = df['CCI'][-dayNum:]
CCI_Row.index = df['trade_date'][-dayNum:].map(
lambda x: "CCI_" + str(x)).tolist()
Kt_Row = df['Kt'][-dayNum:]
Kt_Row.index = df['trade_date'][-dayNum:].map(
lambda x: "Kt_" + str(x)).tolist()
Dt_Row = df['Dt'][-dayNum:]
Dt_Row.index = df['trade_date'][-dayNum:].map(
lambda x: "Dt_" + str(x)).tolist()
Jt_Row = df['Jt'][-dayNum:]
Jt_Row.index = df['trade_date'][-dayNum:].map(
lambda x: "Jt_" + str(x)).tolist()
# 6 读入周复权指数
df_week = pro.weekly(ts_code=code, start_date=start_time2)
# 6-1 如果数据为空
if df_week is None:
print("股票代码", end='')
print(code, end='')
print("的周数据查询结果为空")
continue
# 6-2 数据量小于21
if len(df_week) < 21:
print("股票代码", end='')
print(code, end='')
print("的周数据记录数量小于21，不予计算 DB2")
continue
# 6-3 计算 DB1 和 DB2
df_week['MIN_21'], df_week['MAX_21'], df_week['DB1'] = \
calculateDB1(df_week.high, df_week.low, df_week.close, N, N2, [])
df_week['DB2'] = calculateDB2(df_week['DB1'], N, N2, [])
# 6-4 取出 DB2 作为 series 并取出五天内数据
newRow_week = df_week[-dayNum:]['DB2']
newRow_week.index = weekName
# 7 将数据加入汇总表
# 7-1 合并各指数
allRows = pd.concat([
closeR, closeRow, highRow, newRow, newRow_week, MACD_Row,
RSI_Row, CCI_Row, Kt_Row, Dt_Row, Jt_Row, df_basics
])
allRows.name = code
# print(allRows)
# 7-2 与汇总表 index 对接
newRows = conclude.append(allRows)
if concLen != newRows.shape[1]:
print("股票代码", code, "读取失误，跳过输出，请重新计算")
continue
# 7-3 写入 csv 文件
newRows.to_csv(saveFile,
mode='a',
header=False,
encoding="utf_8_sig",
float_format='%.2f')
print("代码", code, "已记录")
# conclude.to_csv(saveFile, encoding="utf_8_sig", float_format='%.2f')
print("已将汇总表的结果保存在路径: ", saveFile)
MA = pd.concat([MA, local_MA], axis=1)
MACD1, MACD2, XX = ta.MACD(SS.sclose)
MACD = pd.concat([MACD1, MACD2], axis=1)
ADX = ta.ADX(SS.high, SS.low, SS.sclose)
ADXR = ta.ADXR(SS.high, SS.low, SS.sclose)
aroondown, aroonup = ta.AROON(SS.high, SS.low)
ATR = ta.ATR(SS.high, SS.low, SS.sclose)
Bupper, Bmiddle, Blower = ta.BBANDS(SS.sclose)
group1 = pd.concat([
SS, MA, MACD, ADX, ADXR, aroondown, aroonup, ATR, Bupper, Bmiddle, Blower
],
axis=1)
BOP = ta.BOP(SS.sopen, SS.high, SS.low, SS.sclose)
CCI = ta.CCI(SS.high, SS.low, SS.sclose)
CMO = ta.CMO(SS.sclose)
DEMA = ta.DEMA(SS.sclose)
DX = ta.DX(SS.high, SS.low, SS.sclose)
EMA = ta.EMA(SS.sclose)
KAMA = ta.KAMA(SS.sclose)
MFI = ta.MFI(SS.high, SS.low, SS.sclose, SS.volumn)
MOM = ta.MOM(SS.sclose)
RSI = ta.RSI(SS.sclose)
group2 = pd.concat([BOP, CCI, CMO, DEMA, DX, EMA, KAMA, MFI, MOM, RSI], axis=1)
SAR = ta.SAR(SS.high, SS.low)
TEMA = ta.TEMA(SS.sclose)
TRANGE = ta.TRANGE(SS.high, SS.low, SS.sclose)
TRIMA = ta.TRIMA(SS.sclose)
TRIX = ta.TRIX(SS.sclose)
def ta_cci(n, k_data):
cci = pd.DataFrame()
cci['cci'] = ta.CCI(k_data.high, k_data.low, k_data.close, timeperiod=n)
# cci['cci'] = cci.cci.rolling(window=3, min_periods=0, center=False).mean()
return cci.round(2)
def factor_calc(self,
inst_sym='603032.XSHG',
date_time=date(2019, 9, 5),
timeperiod=14,
fastperiod=12,
slowperiod=26,
signalperiod=9):
factor_csv = os.path.join(self.path_factor_, inst_sym[:6] + ".csv")
ohlcvt = self.ohlcvt_data_.load_all_data(inst_sym, date_time,
self.count_)
datetime = ohlcvt["datetime"]
high = ohlcvt["high"]
close = ohlcvt["close"]
low = ohlcvt["low"]
open = ohlcvt["open"]
volumn = ohlcvt["volume"]
factor = talib.ADX(high, low, close, timeperiod)
factor[np.isnan(factor)] = 0
data = pd.DataFrame({"ADX": factor})
#data = pd.concat([data, pd.DataFrame({"ADX": factor})], axis=1)
factor = talib.ADXR(high, low, close, timeperiod)
factor[np.isnan(factor)] = 0
data = pd.concat([data, pd.DataFrame({"ADXR": factor})], axis=1)
factor = talib.APO(close, fastperiod, slowperiod, matype=0)
factor[np.isnan(factor)] = 0
data = pd.concat([data, pd.DataFrame({"APO": factor})], axis=1)
aroondown, aroonup = talib.AROON(high, low, timeperiod)
aroondown[np.isnan(aroondown)] = 0
aroonup[np.isnan(aroonup)] = 0
data = pd.concat([data, pd.DataFrame({"aroondown": aroondown})],
axis=1)
data = pd.concat([data, pd.DataFrame({"aroonup": aroonup})], axis=1)
factor = talib.AROONOSC(high, low, timeperiod)
factor[np.isnan(factor)] = 0
data = pd.concat([data, pd.DataFrame({"AROONOSC": factor})], axis=1)
factor = talib.BOP(open, high, low, close)
factor[np.isnan(factor)] = 0
data = pd.concat([data, pd.DataFrame({"BOP": factor})], axis=1)
factor = talib.CCI(high, low, close, timeperiod)
factor[np.isnan(factor)] = 0
data = pd.concat([data, pd.DataFrame({"CCI": factor})], axis=1)
factor = talib.CMO(close, timeperiod)
factor[np.isnan(factor)] = 0
data = pd.concat([data, pd.DataFrame({"CMO": factor})], axis=1)
factor = talib.DX(high, low, close, timeperiod)
factor[np.isnan(factor)] = 0
data = pd.concat([data, pd.DataFrame({"DX": factor})], axis=1)
[macd, macdsignal, macdhist] = talib.MACD(close, fastperiod,
slowperiod, signalperiod)
macdsignal[np.isnan(macdsignal)] = 0
data = pd.concat([data, pd.DataFrame({"MACD": macdsignal})], axis=1)
factor = talib.MFI(high, low, close, volumn, timeperiod)
factor[np.isnan(factor)] = 0
data = pd.concat([data, pd.DataFrame({"MFI": factor})], axis=1)
factor = talib.PPO(close, fastperiod, slowperiod, matype=0)
factor[np.isnan(factor)] = 0
data = pd.concat([data, pd.DataFrame({"PPO": factor})], axis=1)
factor = talib.ROCP(close, timeperiod)
factor[np.isnan(factor)] = 0
data = pd.concat([data, pd.DataFrame({"ROCP": factor})], axis=1)
factor = talib.RSI(close, timeperiod)
factor[np.isnan(factor)] = 0
data = pd.concat([data, pd.DataFrame({"RSI": factor})], axis=1)
slowk, slowd = talib.STOCH(high,
low,
close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
slowk[np.isnan(slowk)] = 0
slowd[np.isnan(slowd)] = 0
data = pd.concat([data, pd.DataFrame({"STOCHslowk": slowk})], axis=1)
data = pd.concat([data, pd.DataFrame({"STOCHslowd": slowd})], axis=1)
fastk, fastd = talib.STOCHRSI(close,
timeperiod,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
fastk[np.isnan(fastk)] = 0
fastd[np.isnan(fastd)] = 0
data = pd.concat([data, pd.DataFrame({"STOCHRSIfastk": fastk})],
axis=1)
data = pd.concat([data, pd.DataFrame({"STOCHRSIfastd": fastd})],
axis=1)
factor = talib.TRIX(close, timeperiod)
factor[np.isnan(factor)] = 0
data = pd.concat([data, pd.DataFrame({"TRIX": factor})], axis=1)
factor = talib.ULTOSC(high,
low,
close,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
factor[np.isnan(factor)] = 0
data = pd.concat([data, pd.DataFrame({"TRIX": factor})], axis=1)
price = pd.DataFrame(close)
day1 = price.pct_change(periods=1)
data1 = day1.shift(-1, axis=0)
day2 = price.pct_change(periods=2)
day2 = day2.shift(-2, axis=0)
data1 = pd.concat([data1, day2], axis=1)
day3 = price.pct_change(periods=3)
day3 = day3.shift(-3, axis=0)
data1 = pd.concat([data1, day3], axis=1)
day4 = price.pct_change(periods=4)
day4 = day4.shift(-4, axis=0)
data1 = pd.concat([data1, day4], axis=1)
day5 = price.pct_change(periods=5)
day5 = day5.shift(-5, axis=0)
data1 = pd.concat([data1, day5], axis=1)
data1.columns = ["1day", "2day", "3day", "4day", "5day"]
data = pd.concat([data, data1], axis=1)
data[np.isnan(data)] = 0
date_index = pd.DataFrame(datetime).applymap(
lambda x: pd.to_datetime(str(x), format="%Y%m%d%H%M%S"))
date_index.columns = ["datetime"]
data = pd.concat([date_index, data], axis=1)
data.to_csv(factor_csv, index=False)
def appendAllTAData(df=pd.DataFrame([])):
resDF = pd.DataFrame([])
# 函数名：AD名称：ChaikinA/DLine累积/派发线（Accumulation/DistributionLine）
# 简介：MarcChaikin提出的一种平衡交易量指标，以当日的收盘价位来估算成交流量，用于估定一段时间内该证券累积的资金流量。
# 计算公式：A/D=昨日A/D+多空对比*今日成交量多空对比=[（收盘价-最低价）-（最高价-收盘价）]/（最高价-最低价）
# 若最高价等于最低价：多空对比=（收盘价/昨收盘）-1
# 研判：1、A/D测量资金流向，向上的A/D表明买方占优势，而向下的A/D表明卖方占优势
# 2、A/D与价格的背离可视为买卖信号，即底背离考虑买入，顶背离考虑卖出
# 3、应当注意A/D忽略了缺口的影响，事实上，跳空缺口的意义是不能轻易忽略的
# A/D指标无需设置参数，但在应用时，可结合指标的均线进行分析例子：real=AD(high,low,close,volume)
resDF['AD'] = ta.AD(df['max_price'].values, df['min_price'].values,
df['price'].values, df['vol'].values)
# 函数名：ADOSC名称：Chaikin A/D Oscillator Chaikin震荡指标
# 简介：将资金流动情况与价格行为相对比，检测市场中资金流入和流出的情况
# 计算公式：fastperiod A/D - slowperiod A/D
# 研判：1、交易信号是背离：看涨背离做多，看跌背离做空
# 2、股价与90天移动平均结合，与其他指标结合
# 3、由正变负卖出，由负变正买进
# 例子：real = ADOSC(high, low, close, volume, fastperiod=3, slowperiod=10)
resDF['ADOSC'] = ta.ADOSC(df['max_price'].values,
df['min_price'].values,
df['price'].values,
df['vol'].values,
fastperiod=3,
slowperiod=10)
resDF['ADX'] = ta.ADX(df['max_price'].values, df['min_price'].values,
df['price'].values)
resDF['ADXR'] = ta.ADXR(df['max_price'].values,
df['min_price'].values,
df['price'].values,
timeperiod=14)
resDF['APO'] = ta.APO(df['price'].values,
fastperiod=12,
slowperiod=26,
matype=0)
resDF['aroondown'], resDF['aroonup'] = ta.AROON(df['max_price'].values,
df['min_price'].values,
timeperiod=14)
resDF['AROONOSC'] = ta.AROONOSC(df['max_price'].values,
df['min_price'].values,
timeperiod=14)
resDF['ATR'] = ta.ATR(df['max_price'].values,
df['min_price'].values,
df['price'].values,
timeperiod=14)
resDF['AVGPRICE'] = ta.AVGPRICE(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values, df['price'].values)
resDF['upperband'], resDF['middleband'], resDF['lowerband'] = ta.BBANDS(
df['price'].values, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)
resDF['BETA'] = ta.BETA(df['max_price'].values,
df['min_price'].values,
timeperiod=5)
resDF['BOP'] = ta.BOP(df['price_today_open'].values,
df['max_price'].values, df['min_price'].values,
df['price'].values)
resDF['CCI'] = ta.CCI(df['max_price'].values,
df['min_price'].values,
df['price'].values,
timeperiod=10)[-1]
# 函数名：CDL2CROWS名称：Two Crows 两只乌鸦
# 简介：三日K线模式，第一天长阳，第二天高开收阴，第三天再次高开继续收阴，收盘比前一日收盘价低，预示股价下跌。
# 例子：integer = CDL2CROWS(open, high, low, close)
resDF['CDL2CROWS'] = ta.CDL2CROWS(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDL3BLACKCROWS名称：Three Black Crows 三只乌鸦
# 简介：三日K线模式，连续三根阴线，每日收盘价都下跌且接近最低价，每日开盘价都在上根K线实体内，预示股价下跌。
# 例子：integer = CD3BLACKCROWS(open, high, low, close)
resDF['CDL3BLACKCROWS'] = ta.CDL3BLACKCROWS(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDL3INSIDE名称： Three Inside Up/Down 三内部上涨和下跌
# 简介：三日K线模式，母子信号+长K线，以三内部上涨为例，K线为阴阳阳，第三天收盘价高于第一天开盘价，第二天K线在第一天K线内部，预示着股价上涨。
# 例子：integer = CDL3INSIDE(open, high, low, close)
resDF['CDL3INSIDE'] = ta.CDL3INSIDE(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDL3LINESTRIKE名称： Three-Line Strike 三线打击
# 简介：四日K线模式，前三根阳线，每日收盘价都比前一日高，开盘价在前一日实体内，第四日市场高开，收盘价低于第一日开盘价，预示股价下跌。
# 例子：integer = CDL3LINESTRIKE(open, high, low, close)
resDF['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDL3OUTSIDE名称：Three Outside Up/Down 三外部上涨和下跌
# 简介：三日K线模式，与三内部上涨和下跌类似，K线为阴阳阳，但第一日与第二日的K线形态相反，以三外部上涨为例，第一日K线在第二日K线内部，预示着股价上涨。
# 例子：integer = CDL3OUTSIDE(open, high, low, close)
resDF['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDL3STARSINSOUTH名称：Three Stars In The South 南方三星
# 简介：三日K线模式，与大敌当前相反，三日K线皆阴，第一日有长下影线，第二日与第一日类似，K线整体小于第一日，第三日无下影线实体信号，成交价格都在第一日振幅之内，预示下跌趋势反转，股价上升。
# 例子：integer = CDL3STARSINSOUTH(open, high, low, close)
resDF['CDL3STARSINSOUTH'] = ta.CDL3STARSINSOUTH(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDL3WHITESOLDIERS名称：Three Advancing White Soldiers 三个白兵
# 简介：三日K线模式，三日K线皆阳，每日收盘价变高且接近最高价，开盘价在前一日实体上半部，预示股价上升。
# 例子：integer = CDL3WHITESOLDIERS(open, high, low, close)
resDF['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLABANDONEDBABY名称：Abandoned Baby 弃婴
# 简介：三日K线模式，第二日价格跳空且收十字星（开盘价与收盘价接近，最高价最低价相差不大），预示趋势反转，发生在顶部下跌，底部上涨。
# 例子：integer = CDLABANDONEDBABY(open, high, low, close, penetration=0)
resDF['CDLABANDONEDBABY'] = ta.CDLABANDONEDBABY(
df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values,
penetration=0)
# 函数名：CDLADVANCEBLOCK名称：Advance Block 大敌当前
# 简介：三日K线模式，三日都收阳，每日收盘价都比前一日高，开盘价都在前一日实体以内，实体变短，上影线变长。
# 例子：integer = CDLADVANCEBLOCK(open, high, low, close)
resDF['CDLADVANCEBLOCK'] = ta.CDLADVANCEBLOCK(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLBELTHOLD名称：Belt-hold 捉腰带线
# 简介：两日K线模式，下跌趋势中，第一日阴线，第二日开盘价为最低价，阳线，收盘价接近最高价，预示价格上涨。
# 例子：integer = CDLBELTHOLD(open, high, low, close)
resDF['CDLBELTHOLD'] = ta.CDLBELTHOLD(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLBREAKAWAY名称：Breakaway 脱离
# 简介：五日K线模式，以看涨脱离为例，下跌趋势中，第一日长阴线，第二日跳空阴线，延续趋势开始震荡，第五日长阳线，收盘价在第一天收盘价与第二天开盘价之间，预示价格上涨。
# 例子：integer = CDLBREAKAWAY(open, high, low, close)
resDF['CDLBREAKAWAY'] = ta.CDLBREAKAWAY(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名： CDLCLOSINGMARUBOZU 名称：Closing Marubozu 收盘缺影线
# 简介：一日K线模式，以阳线为例，最低价低于开盘价，收盘价等于最高价，预示着趋势持续。
# 例子：integer = CDLCLOSINGMARUBOZU(open, high, low, close)
resDF['CDLCLOSINGMARUBOZU'] = ta.CDLCLOSINGMARUBOZU(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLCONCEALBABYSWALL名称： Concealing Baby Swallow 藏婴吞没
# 简介：四日K线模式，下跌趋势中，前两日阴线无影线，第二日开盘、收盘价皆低于第二日，第三日倒锤头，第四日开盘价高于前一日最高价，收盘价低于前一日最低价，预示着底部反转。
# 例子：integer = CDLCONCEALBABYSWALL(open, high, low, close)
resDF['CDLCONCEALBABYSWALL'] = ta.CDLCONCEALBABYSWALL(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLCOUNTERATTACK
# 名称：Counterattack 反击线
# 简介：二日K线模式，与分离线类似。
# 例子：integer = CDLCOUNTERATTACK(open, high, low, close)
resDF['CDLCOUNTERATTACK'] = ta.CDLCOUNTERATTACK(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLDARKCLOUDCOVER名称：Dark Cloud Cover 乌云压顶
# 简介：二日K线模式，第一日长阳，第二日开盘价高于前一日最高价，收盘价处于前一日实体中部以下，预示着股价下跌。
# 例子：integer = CDLDARKCLOUDCOVER(open, high, low, close, penetration=0)
resDF['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(
df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values,
penetration=0)
# 函数名： CDLDOJI
# 名称：Doji 十字
# 简介：一日K线模式，开盘价与收盘价基本相同。
# 例子：integer = CDLDOJI(open, high, low, close)
resDF['CDLDOJI'] = ta.CDLDOJI(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名： CDLDOJISTAR
# 名称：Doji Star 十字星
# 简介：一日K线模式，开盘价与收盘价基本相同，上下影线不会很长，预示着当前趋势反转。
# 例子：integer = CDLDOJISTAR(open, high, low, close)
resDF['CDLDOJISTAR'] = ta.CDLDOJISTAR(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLDRAGONFLYDOJI名称：Dragonfly Doji 蜻蜓十字/T形十字
# 简介：一日K线模式，开盘后价格一路走低，之后收复，收盘价与开盘价相同，预示趋势反转。
# 例子：integer = CDLDRAGONFLYDOJI(open, high, low, close)
resDF['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLENGULFING名称：Engulfing Pattern 吞噬模式
# 简介：两日K线模式，分多头吞噬和空头吞噬，以多头吞噬为例，第一日为阴线，第二日阳线，第一日的开盘价和收盘价在第二日开盘价收盘价之内，但不能完全相同。
# 例子：integer = CDLENGULFING(open, high, low, close)
resDF['CDLENGULFING'] = ta.CDLENGULFING(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLEVENINGDOJISTAR名称：Evening Doji Star 十字暮星
# 简介：三日K线模式，基本模式为暮星，第二日收盘价和开盘价相同，预示顶部反转。
# 例子：integer = CDLEVENINGDOJISTAR(open, high, low, close, penetration=0)
resDF['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(
df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values,
penetration=0)
# 函数名：CDLEVENINGSTAR名称：Evening Star 暮星
# 简介：三日K线模式，与晨星相反，上升趋势中，第一日阳线，第二日价格振幅较小，第三日阴线，预示顶部反转。
# 例子：integer = CDLEVENINGSTAR(open, high, low, close, penetration=0)
resDF['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values,
penetration=0)
# 函数名：CDLGAPSIDESIDEWHITE名称：Up/Down-gap side-by-side white lines 向上/下跳空并列阳线
# 简介：二日K线模式，上升趋势向上跳空，下跌趋势向下跳空，第一日与第二日有相同开盘价，实体长度差不多，则趋势持续。
# 例子：integer = CDLGAPSIDESIDEWHITE(open, high, low, close)
resDF['CDLGAPSIDESIDEWHITE'] = ta.CDLGAPSIDESIDEWHITE(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLGRAVESTONEDOJI名称：Gravestone Doji 墓碑十字/倒T十字
# 简介：一日K线模式，开盘价与收盘价相同，上影线长，无下影线，预示底部反转。
# 例子：integer = CDLGRAVESTONEDOJI(open, high, low, close)
resDF['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLHAMMER
# 名称：Hammer 锤头
# 简介：一日K线模式，实体较短，无上影线，下影线大于实体长度两倍，处于下跌趋势底部，预示反转。
# 例子：integer = CDLHAMMER(open, high, low, close)
resDF['CDLHAMMER'] = ta.CDLHAMMER(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLHANGINGMAN
# 名称：Hanging Man 上吊线
# 简介：一日K线模式，形状与锤子类似，处于上升趋势的顶部，预示着趋势反转。
# 例子：integer = CDLHANGINGMAN(open, high, low, close)
resDF['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLHARAMI名称：Harami Pattern 母子线
# 简介：二日K线模式，分多头母子与空头母子，两者相反，以多头母子为例，在下跌趋势中，第一日K线长阴，第二日开盘价收盘价在第一日价格振幅之内，为阳线，预示趋势反转，股价上升。
# 例子：integer = CDLHARAMI(open, high, low, close)
resDF['CDLHARAMI'] = ta.CDLHARAMI(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLHARAMICROSS名称：Harami Cross Pattern 十字孕线
# 简介：二日K线模式，与母子县类似，若第二日K线是十字线，便称为十字孕线，预示着趋势反转。
# 例子：integer = CDLHARAMICROSS(open, high, low, close)
resDF['CDLHARAMICROSS'] = ta.CDLHARAMICROSS(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLHIGHWAVE
# 名称：High-Wave Candle 风高浪大线
# 简介：三日K线模式，具有极长的上/下影线与短的实体，预示着趋势反转。
# 例子：integer = CDLHIGHWAVE(open, high, low, close)
resDF['CDLHIGHWAVE'] = ta.CDLHIGHWAVE(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLHIKKAKE名称：Hikkake Pattern 陷阱
# 简介：三日K线模式，与母子类似，第二日价格在前一日实体范围内，第三日收盘价高于前两日，反转失败，趋势继续。
# 例子：integer = CDLHIKKAKE(open, high, low, close)
resDF['CDLHIKKAKE'] = ta.CDLHIKKAKE(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLHIKKAKEMOD名称：Modified Hikkake Pattern 修正陷阱
# 简介：三日K线模式，与陷阱类似，上升趋势中，第三日跳空高开；下跌趋势中，第三日跳空低开，反转失败，趋势继续。
# 例子：integer = CDLHIKKAKEMOD(open, high, low, close)
resDF['CDLHIKKAKEMOD'] = ta.CDLHIKKAKEMOD(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLHOMINGPIGEON名称：Homing Pigeon 家鸽
# 简介：二日K线模式，与母子线类似，不同的的是二日K线颜色相同，第二日最高价、最低价都在第一日实体之内，预示着趋势反转。
# 例子：integer = CDLHOMINGPIGEON(open, high, low, close)
resDF['CDLHOMINGPIGEON'] = ta.CDLHOMINGPIGEON(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLIDENTICAL3CROWS名称：Identical Three Crows 三胞胎乌鸦
# 简介：三日K线模式，上涨趋势中，三日都为阴线，长度大致相等，每日开盘价等于前一日收盘价，收盘价接近当日最低价，预示价格下跌。
# 例子：integer = CDLIDENTICAL3CROWS(open, high, low, close)
resDF['CDLIDENTICAL3CROWS'] = ta.CDLIDENTICAL3CROWS(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLINNECK名称：In-Neck Pattern 颈内线
# 简介：二日K线模式，下跌趋势中，第一日长阴线，第二日开盘价较低，收盘价略高于第一日收盘价，阳线，实体较短，预示着下跌继续。
# 例子：integer = CDLINNECK(open, high, low, close)
resDF['CDLINNECK'] = ta.CDLINNECK(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLINVERTEDHAMMER名称：Inverted Hammer 倒锤头
# 简介：一日K线模式，上影线较长，长度为实体2倍以上，无下影线，在下跌趋势底部，预示着趋势反转。
# 例子：integer = CDLINVERTEDHAMMER(open, high, low, close)
resDF['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLKICKING
# 名称：Kicking 反冲形态
# 简介：二日K线模式，与分离线类似，两日K线为秃线，颜色相反，存在跳空缺口。
# 例子：integer = CDLKICKING(open, high, low, close)
resDF['CDLKICKING'] = ta.CDLKICKING(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLKICKINGBYLENGTH名称：Kicking - bull/bear determined by the longer marubozu 由较长缺影线决定的反冲形态
# 简介：二日K线模式，与反冲形态类似，较长缺影线决定价格的涨跌。
# 例子：integer = CDLKICKINGBYLENGTH(open, high, low, close)
resDF['CDLKICKINGBYLENGTH'] = ta.CDLKICKINGBYLENGTH(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLLADDERBOTTOM名称：Ladder Bottom 梯底
# 简介：五日K线模式，下跌趋势中，前三日阴线，开盘价与收盘价皆低于前一日开盘、收盘价，第四日倒锤头，第五日开盘价高于前一日开盘价，阳线，收盘价高于前几日价格振幅，预示着底部反转。
# 例子：integer = CDLLADDERBOTTOM(open, high, low, close)
resDF['CDLLADDERBOTTOM'] = ta.CDLLADDERBOTTOM(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLLONGLEGGEDDOJI名称：Long Legged Doji 长脚十字
# 简介：一日K线模式，开盘价与收盘价相同居当日价格中部，上下影线长，表达市场不确定性。
# 例子：integer = CDLLONGLEGGEDDOJI(open, high, low, close)
resDF['CDLLONGLEGGEDDOJI'] = ta.CDLLONGLEGGEDDOJI(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLLONGLINE
# 名称：Long Line Candle 长蜡烛
# 简介：一日K线模式，K线实体长，无上下影线。
# 例子：integer = CDLLONGLINE(open, high, low, close)
resDF['CDLLONGLINE'] = ta.CDLLONGLINE(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLMARUBOZU
# 名称：Marubozu 光头光脚/缺影线
# 简介：一日K线模式，上下两头都没有影线的实体，阴线预示着熊市持续或者牛市反转，阳线相反。
# 例子：integer = CDLMARUBOZU(open, high, low, close)
resDF['CDLMARUBOZU'] = ta.CDLMARUBOZU(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLMATCHINGLOW名称：Matching Low 相同低价
# 简介：二日K线模式，下跌趋势中，第一日长阴线，第二日阴线，收盘价与前一日相同，预示底部确认，该价格为支撑位。
# 例子：integer = CDLMATCHINGLOW(open, high, low, close)
resDF['CDLMATCHINGLOW'] = ta.CDLMATCHINGLOW(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLMATHOLD名称：Mat Hold 铺垫
# 简介：五日K线模式，上涨趋势中，第一日阳线，第二日跳空高开影线，第三、四日短实体影线，第五日阳线，收盘价高于前四日，预示趋势持续。
# 例子：integer = CDLMATHOLD(open, high, low, close, penetration=0)
resDF['CDLMATHOLD'] = ta.CDLMATHOLD(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values,
penetration=0)
# 函数名：CDLMORNINGDOJISTAR名称：Morning Doji Star 十字晨星
# 简介：三日K线模式，基本模式为晨星，第二日K线为十字星，预示底部反转。
# 例子：integer = CDLMORNINGDOJISTAR(open, high, low, close, penetration=0)
resDF['CDLMORNINGDOJISTAR'] = ta.CDLMORNINGDOJISTAR(
df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values,
penetration=0)
# 函数名：CDLMORNINGSTAR名称：Morning Star 晨星
# 简介：三日K线模式，下跌趋势，第一日阴线，第二日价格振幅较小，第三天阳线，预示底部反转。
# 例子：integer = CDLMORNINGSTAR(open, high, low, close, penetration=0)
resDF['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values,
penetration=0)
# 函数名：CDLONNECK名称：On-Neck Pattern 颈上线
# 简介：二日K线模式，下跌趋势中，第一日长阴线，第二日开盘价较低，收盘价与前一日最低价相同，阳线，实体较短，预示着延续下跌趋势。
# 例子：integer = CDLONNECK(open, high, low, close)
resDF['CDLONNECK'] = ta.CDLONNECK(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLPIERCING名称：Piercing Pattern 刺透形态
# 简介：两日K线模式，下跌趋势中，第一日阴线，第二日收盘价低于前一日最低价，收盘价处在第一日实体上部，预示着底部反转。
# 例子：integer = CDLPIERCING(open, high, low, close)
resDF['CDLPIERCING'] = ta.CDLPIERCING(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLRICKSHAWMAN名称：Rickshaw Man 黄包车夫
# 简介：一日K线模式，与长腿十字线类似，若实体正好处于价格振幅中点，称为黄包车夫。
# 例子：integer = CDLRICKSHAWMAN(open, high, low, close)
resDF['CDLRICKSHAWMAN'] = ta.CDLRICKSHAWMAN(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLRISEFALL3METHODS名称：Rising/Falling Three Methods 上升/下降三法
# 简介： 五日K线模式，以上升三法为例，上涨趋势中，第一日长阳线，中间三日价格在第一日范围内小幅震荡，第五日长阳线，收盘价高于第一日收盘价，预示股价上升。
# 例子：integer = CDLRISEFALL3METHODS(open, high, low, close)
resDF['CDLRISEFALL3METHODS'] = ta.CDLRISEFALL3METHODS(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLSEPARATINGLINES名称：Separating Lines 分离线
# 简介：二日K线模式，上涨趋势中，第一日阴线，第二日阳线，第二日开盘价与第一日相同且为最低价，预示着趋势继续。
# 例子：integer = CDLSEPARATINGLINES(open, high, low, close)
resDF['CDLSEPARATINGLINES'] = ta.CDLSEPARATINGLINES(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLSHOOTINGSTAR名称：Shooting Star 射击之星
# 简介：一日K线模式，上影线至少为实体长度两倍，没有下影线，预示着股价下跌
# 例子：integer = CDLSHOOTINGSTAR(open, high, low, close)
resDF['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLSHORTLINE
# 名称：Short Line Candle 短蜡烛
# 简介：一日K线模式，实体短，无上下影线。
# 例子：integer = CDLSHORTLINE(open, high, low, close)
resDF['CDLSHORTLINE'] = ta.CDLSHORTLINE(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLSPINNINGTOP
# 名称：Spinning Top 纺锤
# 简介：一日K线，实体小。
# 例子：integer = CDLSPINNINGTOP(open, high, low, close)
resDF['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLSTALLEDPATTERN名称：Stalled Pattern 停顿形态
# 简介：三日K线模式，上涨趋势中，第二日长阳线，第三日开盘于前一日收盘价附近，短阳线，预示着上涨结束。
# 例子：integer = CDLSTALLEDPATTERN(open, high, low, close)
resDF['CDLSTALLEDPATTERN'] = ta.CDLSTALLEDPATTERN(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLSTICKSANDWICH名称：Stick Sandwich 条形三明治
# 简介：三日K线模式，第一日长阴线，第二日阳线，开盘价高于前一日收盘价，第三日开盘价高于前两日最高价，收盘价于第一日收盘价相同。
# 例子：integer = CDLSTICKSANDWICH(open, high, low, close)
resDF['CDLSTICKSANDWICH'] = ta.CDLSTICKSANDWICH(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLTAKURI名称：Takuri (Dragonfly Doji with very long lower shadow) 探水竿
# 简介：一日K线模式，大致与蜻蜓十字相同，下影线长度长。
# 例子：integer = CDLTAKURI(open, high, low, close)
resDF['CDLTAKURI'] = ta.CDLTAKURI(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLTASUKIGAP名称：Tasuki Gap 跳空并列阴阳线
# 简介：三日K线模式，分上涨和下跌，以上升为例，前两日阳线，第二日跳空，第三日阴线，收盘价于缺口中，上升趋势持续。
# 例子：integer = CDLTASUKIGAP(open, high, low, close)
resDF['CDLTASUKIGAP'] = ta.CDLTASUKIGAP(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLTHRUSTING名称：Thrusting Pattern 插入
# 简介：二日K线模式，与颈上线类似，下跌趋势中，第一日长阴线，第二日开盘价跳空，收盘价略低于前一日实体中部，与颈上线相比实体较长，预示着趋势持续。
# 例子：integer = CDLTHRUSTING(open, high, low, close)
resDF['CDLTHRUSTING'] = ta.CDLTHRUSTING(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLTRISTAR
# 名称：Tristar Pattern 三星
# 简介：三日K线模式，由三个十字组成，第二日十字必须高于或者低于第一日和第三日，预示着反转。
# 例子：integer = CDLTRISTAR(open, high, low, close)
resDF['CDLTRISTAR'] = ta.CDLTRISTAR(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values)
# 函数名：CDLUNIQUE3RIVER名称：Unique 3 River 奇特三河床
# 简介：三日K线模式，下跌趋势中，第一日长阴线，第二日为锤头，最低价创新低，第三日开盘价低于第二日收盘价，收阳线，收盘价不高于第二日收盘价，预示着反转，第二日下影线越长可能性越大。
# 例子：integer = CDLUNIQUE3RIVER(open, high, low, close)
resDF['CDLUNIQUE3RIVER'] = ta.CDLUNIQUE3RIVER(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLUPSIDEGAP2CROWS名称：Upside Gap Two Crows 向上跳空的两只乌鸦
# 简介：三日K线模式，第一日阳线，第二日跳空以高于第一日最高价开盘，收阴线，第三日开盘价高于第二日，收阴线，与第一日比仍有缺口。
# 例子：integer = CDLUPSIDEGAP2CROWS(open, high, low, close)
resDF['CDLUPSIDEGAP2CROWS'] = ta.CDLUPSIDEGAP2CROWS(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
# 函数名：CDLXSIDEGAP3METHODS名称：Upside/Downside Gap Three Methods 上升/下降跳空三法
# 简介：五日K线模式，以上升跳空三法为例，上涨趋势中，第一日长阳线，第二日短阳线，第三日跳空阳线，第四日阴线，开盘价与收盘价于前两日实体内，第五日长阳线，收盘价高于第一日收盘价，预示股价上升。
# 例子：integer = CDLXSIDEGAP3METHODS(open, high, low, close)
resDF['CDLXSIDEGAP3METHODS'] = ta.CDLXSIDEGAP3METHODS(
df['price_today_open'].values, df['max_price'].values,
df['min_price'].values, df['price'].values)
resDF['CMO'] = ta.CMO(df['price'].values, timeperiod=14)
resDF['CORREL'] = ta.CORREL(df['max_price'].values,
df['min_price'].values,
timeperiod=30)
resDF['DEMA'] = ta.DEMA(df['price'].values, timeperiod=30)
resDF['DX'] = ta.DX(df['max_price'].values,
df['min_price'].values,
df['price'].values,
timeperiod=14)
resDF['EMA'] = ta.EMA(df['price'].values, timeperiod=30)
resDF['HT_DCPERIOD'] = ta.HT_DCPERIOD(df['price'].values)
resDF['HT_DCPHASE'] = ta.HT_DCPHASE(df['price'].values)
resDF['inphase'], resDF['quadrature'] = ta.HT_PHASOR(df['price'].values)
resDF['sine'], resDF['leadsine'] = ta.HT_SINE(df['price'].values)
resDF['HT_TRENDLINE'] = ta.HT_TRENDLINE(df['price'].values)
resDF['HT_TRENDMODE'] = ta.HT_TRENDMODE(df['price'].values)
resDF['KAMA'] = ta.KAMA(df['price'].values, timeperiod=30)
resDF['LINEARREG'] = ta.LINEARREG(df['price'].values, timeperiod=14)
resDF['LINEARREG_ANGLE'] = ta.LINEARREG_ANGLE(df['price'].values,
timeperiod=14)
resDF['LINEARREG_INTERCEPT'] = ta.LINEARREG_INTERCEPT(df['price'].values,
timeperiod=14)
resDF['LINEARREG_SLOPE'] = ta.LINEARREG_SLOPE(df['price'].values,
timeperiod=14)
resDF['MA'] = ta.MA(df['price'].values, timeperiod=30, matype=0)
resDF['macd'], resDF['macdsignal'], resDF['macdhist'] = ta.MACD(
df['price'].values, fastperiod=12, slowperiod=26, signalperiod=9)
resDF['macd'], resDF['macdsignal'], resDF['macdhist'] = ta.MACDEXT(
df['price'].values,
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0)
resDF['macd'], resDF['macdsignal'], resDF['macdhist'] = ta.MACDFIX(
df['price'].values, signalperiod=9)
#resDF['mama'], resDF['fama'] = ta.MAMA (df['price'].values, fastlimit=0, slowlimit=0)
resDF['MAX'] = ta.MAX(df['price'].values, timeperiod=30)
resDF['MAXINDEX'] = ta.MAXINDEX(df['price'].values, timeperiod=30)
resDF['MEDPRICE'] = ta.MEDPRICE(df['max_price'].values,
df['min_price'].values)
resDF['MFI'] = ta.MFI(df['price_today_open'].values,
df['max_price'].values,
df['min_price'].values,
df['price'].values,
timeperiod=14)
resDF['MIDPOINT'] = ta.MIDPOINT(df['price'].values, timeperiod=14)
resDF['MIDPRICE'] = ta.MIDPRICE(df['max_price'].values,
df['min_price'].values,
timeperiod=14)
resDF['MIN'] = ta.MIN(df['price'].values, timeperiod=30)
resDF['MININDEX'] = ta.MININDEX(df['price'].values, timeperiod=30)
resDF['min'], resDF['max'] = ta.MINMAX(df['price'].values, timeperiod=30)
resDF['minidx'], resDF['maxidx'] = ta.MINMAXINDEX(df['price'].values,
timeperiod=30)
resDF['MINUS_DI'] = ta.MINUS_DI(df['max_price'].values,
df['min_price'].values,
df['price'].values,
timeperiod=14)
resDF['MINUS_DM'] = ta.MINUS_DM(df['max_price'].values,
df['min_price'].values,
timeperiod=14)
resDF['MOM'] = ta.MOM(df['max_price'].values, timeperiod=10)
resDF['NATR'] = ta.NATR(df['max_price'].values,
df['min_price'].values,
df['price'].values,
timeperiod=14)
# 函数名：OBV 名称：On Balance Volume 能量潮
# 简介：Joe Granville提出，通过统计成交量变动的趋势推测股价趋势计算公式：以某日为基期，逐日累计每日上市股票总成交量，若隔日指数或股票上涨，则基期OBV加上本日成交量为本日OBV。隔日指数或股票下跌，则基期OBV减去本日成交量为本日OBV
# 研判：1、以“N”字型为波动单位，一浪高于一浪称“上升潮”，下跌称“跌潮”；上升潮买进，跌潮卖出
# 2、须配合K线图走势
# 3、用多空比率净额法进行修正，但不知TA-Lib采用哪种方法
# 多空比率净额= [（收盘价－最低价）－（最高价-收盘价）] ÷（ 最高价－最低价）×成交量
# 例子：real = OBV(close, volume)
resDF['OBV'] = ta.OBV(df['price'].values, df['vol'].values)
# resDF['PLUS_DI'] = ta.PLUS_DI
# resDF['PLUS_DM'] = ta.PLUS_DM
resDF['PPO'] = ta.PPO(df['price'].values,
fastperiod=12,
slowperiod=26,
matype=0)
resDF['ROC'] = ta.ROC(df['price'].values, timeperiod=10)
resDF['ROCP'] = ta.ROCP(df['price'].values, timeperiod=10)
resDF['ROCR'] = ta.ROCR(df['price'].values, timeperiod=10)
resDF['ROCR100'] = ta.ROCR100(df['price'].values, timeperiod=10)
resDF['RSI'] = ta.RSI(df['price'].values, timeperiod=14)
resDF['SAR'] = ta.SAR(df['max_price'].values,
df['min_price'].values,
acceleration=0,
maximum=0)
resDF['SAREXT'] = ta.SAREXT(df['max_price'].values,
df['min_price'].values,
startvalue=0,
offsetonreverse=0,
accelerationinitlong=0,
accelerationlong=0,
accelerationmaxlong=0,
accelerationinitshort=0,
accelerationshort=0,
accelerationmaxshort=0)
resDF['SMA'] = ta.SMA(df['price'].values, timeperiod=30)
resDF['STDDEV'] = ta.STDDEV(df['price'].values, timeperiod=5, nbdev=1)
# resDF['STOCH'] = ta.STOCH
# resDF['STOCHF'] = ta.STOCHF
resDF['fastk'], resDF['fastd'] = ta.STOCHRSI(df['price'].values,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
resDF['SUM'] = ta.SUM(df['price'].values, timeperiod=30)
resDF['T3'] = ta.T3(df['price'].values, timeperiod=5, vfactor=0)
resDF['TEMA'] = ta.TEMA(df['price'].values, timeperiod=30)
resDF['TRANGE'] = ta.TRANGE(df['max_price'].values, df['min_price'].values,
df['price'].values)
resDF['TRIMA'] = ta.TRIMA(df['price'].values, timeperiod=30)
resDF['TRIX'] = ta.TRIX(df['price'].values, timeperiod=30)
resDF['TSF'] = ta.TSF(df['price'].values, timeperiod=14)
resDF['TYPPRICE'] = ta.TYPPRICE(df['max_price'].values,
df['min_price'].values, df['price'].values)
# resDF['ULTOSC'] = ta.ULTOSC
resDF['VAR'] = ta.VAR(df['price'].values, timeperiod=5, nbdev=1)
resDF['WCLPRICE'] = ta.WCLPRICE(df['max_price'].values,
df['min_price'].values, df['price'].values)
# resDF['WILLR'] = ta.WILLR
resDF['WMA'] = ta.WMA(df['price'].values, timeperiod=30)
return resDF
def get_datasets(symbol, to_symbol, histo, limit):
"""Fetch the API and precess the desired pair
Arguments:
symbol {str} -- First pair
to_symbol {str} -- Second pair
histo {str ['day', 'hour']} -- Granularity
limit {int [100 - 2000]} -- [description]
Returns:
pandas.Dataframe -- The OHLCV and indicators dataframe
"""
headers = {
'User-Agent':
'Mozilla/5.0',
'authorization':
'Apikey 3d7d3e9e6006669ac00584978342451c95c3c78421268ff7aeef69995f9a09ce'
}
# OHLC
url = 'https://min-api.cryptocompare.com/data/histo{}?fsym={}&tsym={}&e=Binance&limit={}'.format(
histo, symbol, to_symbol, limit)
print(colored('> downloading ' + symbol + ' OHLCV', 'green'))
response = requests.get(url, headers=headers)
json_response = response.json()
status = json_response['Response']
if status == "Error":
print(colored('=== {} ==='.format(json_response['Message']), 'red'))
raise AssertionError()
result = json_response['Data']
df = pd.DataFrame(result)
df['Date'] = pd.to_datetime(df['time'], utc=True, unit='s')
df.drop('time', axis=1, inplace=True)
# indicators
# https://github.com/mrjbq7/ta-lib/blob/master/docs/func.md
open_price, high, low, close = np.array(df['open']), np.array(
df['high']), np.array(df['low']), np.array(df['close'])
volume = np.array(df['volumefrom'])
# cycle indicators
df.loc[:, 'HT_DCPERIOD'] = talib.HT_DCPERIOD(close)
df.loc[:, 'HT_DCPHASE'] = talib.HT_DCPHASE(close)
df.loc[:,
'HT_PHASOR_inphase'], df.loc[:,
'HT_PHASOR_quadrature'] = talib.HT_PHASOR(
close)
df.loc[:,
'HT_SINE_sine'], df.loc[:,
'HT_SINE_leadsine'] = talib.HT_SINE(close)
df.loc[:, 'HT_TRENDMODE'] = talib.HT_TRENDMODE(close)
# momemtum indicators
df.loc[:, 'ADX'] = talib.ADX(high, low, close, timeperiod=14)
df.loc[:, 'ADXR'] = talib.ADXR(high, low, close, timeperiod=14)
df.loc[:, 'APO'] = talib.APO(close, fastperiod=12, slowperiod=26, matype=0)
df.loc[:, 'AROON_down'], df.loc[:, 'AROON_up'] = talib.AROON(high,
low,
timeperiod=14)
df.loc[:, 'AROONOSC'] = talib.AROONOSC(high, low, timeperiod=14)
df.loc[:, 'BOP'] = talib.BOP(open_price, high, low, close)
df.loc[:, 'CCI'] = talib.CCI(high, low, close, timeperiod=14)
df.loc[:, 'CMO'] = talib.CMO(close, timeperiod=14)
df.loc[:, 'DX'] = talib.DX(high, low, close, timeperiod=14)
df['MACD'], df['MACD_signal'], df['MACD_hist'] = talib.MACD(close,
fastperiod=12,
slowperiod=26,
signalperiod=9)
df.loc[:, 'MFI'] = talib.MFI(high, low, close, volume, timeperiod=14)
df.loc[:, 'MINUS_DI'] = talib.MINUS_DI(high, low, close, timeperiod=14)
df.loc[:, 'MINUS_DM'] = talib.MINUS_DM(high, low, timeperiod=14)
df.loc[:, 'MOM'] = talib.MOM(close, timeperiod=10)
df.loc[:, 'PPO'] = talib.PPO(close, fastperiod=12, slowperiod=26, matype=0)
df.loc[:, 'ROC'] = talib.ROC(close, timeperiod=10)
df.loc[:, 'RSI'] = talib.RSI(close, timeperiod=14)
df.loc[:, 'STOCH_k'], df.loc[:, 'STOCH_d'] = talib.STOCH(high,
low,
close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
df.loc[:, 'STOCHF_k'], df.loc[:, 'STOCHF_d'] = talib.STOCHF(high,
low,
close,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
df.loc[:, 'STOCHRSI_K'], df.loc[:, 'STOCHRSI_D'] = talib.STOCHRSI(
close, timeperiod=30, fastk_period=14, fastd_period=10, fastd_matype=1)
df.loc[:, 'TRIX'] = talib.TRIX(close, timeperiod=30)
df.loc[:, 'ULTOSC'] = talib.ULTOSC(high,
low,
close,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
df.loc[:, 'WILLR'] = talib.WILLR(high, low, close, timeperiod=14)
# overlap studies
df.loc[:,
'BBANDS_upper'], df.loc[:,
'BBANDS_middle'], df.loc[:,
'BBANDS_lower'] = talib.BBANDS(
close,
timeperiod=5,
nbdevup=2,
nbdevdn=2,
matype=0)
df.loc[:, 'DEMA'] = talib.DEMA(close, timeperiod=30)
df.loc[:, 'EMA'] = talib.EMA(close, timeperiod=30)
df.loc[:, 'HT_TRENDLINE'] = talib.HT_TRENDLINE(close)
df.loc[:, 'KAMA'] = talib.KAMA(close, timeperiod=30)
df.loc[:, 'MA'] = talib.MA(close, timeperiod=30, matype=0)
df.loc[:, 'MIDPOINT'] = talib.MIDPOINT(close, timeperiod=14)
df.loc[:, 'WMA'] = talib.WMA(close, timeperiod=30)
df.loc[:, 'SMA'] = talib.SMA(close)
# pattern recoginition
df.loc[:, 'CDL2CROWS'] = talib.CDL2CROWS(open_price, high, low, close)
df.loc[:, 'CDL3BLACKCROWS'] = talib.CDL3BLACKCROWS(open_price, high, low,
close)
df.loc[:, 'CDL3INSIDE'] = talib.CDL3INSIDE(open_price, high, low, close)
df.loc[:, 'CDL3LINESTRIKE'] = talib.CDL3LINESTRIKE(open_price, high, low,
close)
# price transform
df.loc[:, 'WCLPRICE'] = talib.WCLPRICE(high, low, close)
# statistic funcitons
df.loc[:, 'BETA'] = talib.BETA(high, low, timeperiod=5)
df.loc[:, 'CORREL'] = talib.CORREL(high, low, timeperiod=30)
df.loc[:, 'STDDEV'] = talib.STDDEV(close, timeperiod=5, nbdev=1)
df.loc[:, 'TSF'] = talib.TSF(close, timeperiod=14)
df.loc[:, 'VAR'] = talib.VAR(close, timeperiod=5, nbdev=1)
# volatility indicators
df.loc[:, 'ATR'] = talib.ATR(high, low, close, timeperiod=14)
df.loc[:, 'NATR'] = talib.NATR(high, low, close, timeperiod=14)
df.loc[:, 'TRANGE'] = talib.TRANGE(high, low, close)
# volume indicators
df.loc[:, 'AD'] = talib.AD(high, low, close, volume)
df.loc[:, 'ADOSC'] = talib.ADOSC(high,
low,
close,
volume,
fastperiod=3,
slowperiod=10)
df.loc[:, 'OBV'] = talib.OBV(close, volume)
# wallet indicator to trading bot
# df.loc[:, 'wallet_{}'.format(symbol)] = 1.0
df.loc[:, 'wallet_first_symbol'] = WALLET_FIRST_SYMBOL
df.loc[:, 'wallet_second_symbol'] = WALLET_SECOND_SYMBOL
# df.loc[:, 'wallet_{}'.format(to_symbol)] = 0.0
# df.fillna(df.mean(), inplace=True)
df.dropna(inplace=True)
df.set_index('Date', inplace=True)
train_size = round(len(df) *
0.5) # 50% to train -> test with different value
df_train = df[:train_size]
df_rollout = df[train_size:]
df_train.to_csv('datasets/bot_train_{}_{}_{}.csv'.format(
symbol + to_symbol, limit, histo))
df_rollout.to_csv('datasets/bot_rollout_{}_{}_{}.csv'.format(
symbol + to_symbol, limit, histo))
return df
for file in file_list:
path = "C:/Users/pmsk9/Desktop/2021년도 연구/시가총액100위"
df.append(pd.read_csv(path+"/"+file))
for i in df:
ADX=talib.ADX(i.High,i.Low,i.Close,timeperiod=14)
aroondown,aroonup =talib.AROON(i.High, i.Low, timeperiod=14)
AROONOSC=talib.AROONOSC(i.High,i.Low,timeperiod=14)
BOP=talib.BOP(i.Open,i.High,i.Low,i.Close)
CCI=talib.CCI(i.High,i.Low,i.Close,timeperiod=9)
CMO=talib.CMO(i.Close,timeperiod=14)
DX=talib.DX(i.High,i.Low,i.Close,timeperiod=14)
MFI=talib.MFI(i.High, i.Low,i.Close, i.Volume, timeperiod=14)
PPO=talib.PPO(i.Close, fastperiod=12, slowperiod=26, matype=0)
ROC=talib.ROC(i.Close,timeperiod=10)
RSI=talib.RSI(i.Close,timeperiod=14)
slowk, slowd = talib.STOCH(i.High, i.Low, i.Close, fastk_period=12.5, slowk_period=5, slowk_matype=0, slowd_period=3, slowd_matype=0)
def cci(self):
df = self.select_k()
cci = talib.CCI(df.high, df.low, df.close, timeperiod=14)
#print(len(cci))
return cci
def generate_tech_data(stock,
open_name,
close_name,
high_name,
low_name,
max_time_window=10):
open_price = stock[open_name].values
close_price = stock[close_name].values
low_price = stock[low_name].values
high_price = stock[high_name].values
data = pd.DataFrame(stock)
data['MOM'] = talib.MOM(close_price, timeperiod=max_time_window)
# data['_SMA'] = talib.SMA(close_price)
data['HT_DCPERIOD'] = talib.HT_DCPERIOD(close_price)
data['HT_DCPHASE'] = talib.HT_DCPHASE(close_price)
data['sine'], data['leadsine'] = talib.HT_SINE(close_price)
data['inphase'], data['quadrature'] = talib.HT_PHASOR(close_price)
# data['_HT_TRENDMODE'] = talib.HT_TRENDMODE(close_price)
# data['_SAREXT'] = talib.SAREXT(high_price, low_price)
# data['_ADX'] = talib.ADX(high_price, low_price, close_price)
data['ADXR'] = talib.ADXR(high_price,
low_price,
close_price,
timeperiod=max_time_window)
data['APO'] = talib.APO(close_price,
fastperiod=max_time_window // 2,
slowperiod=max_time_window)
data['AROON_UP'], _ = talib.AROON(high_price,
low_price,
timeperiod=max_time_window)
# data['_BOP'] = talib.BOP(open_price, high_price, low_price, close_price)
data['CCI'] = talib.CCI(high_price,
low_price,
close_price,
timeperiod=max_time_window)
data['PLUS_DI'] = talib.PLUS_DI(high_price,
low_price,
close_price,
timeperiod=max_time_window)
# data['_PLUS_DM'] = talib.PLUS_DM(high_price, low_price)
data['PPO'] = talib.PPO(close_price,
fastperiod=max_time_window // 2,
slowperiod=max_time_window)
data['macd'], data['macd_sig'], data['macd_hist'] = talib.MACD(
close_price,
fastperiod=max_time_window // 2,
slowperiod=max_time_window,
signalperiod=max_time_window // 2)
data['CMO'] = talib.CMO(close_price, timeperiod=max_time_window)
# data['ROC'] = talib.ROC(close_price)
data['ROCP'] = talib.ROCP(close_price, timeperiod=max_time_window)
# data['ROCR'] = talib.ROCR(close_price)
# data['slowk'], data['slowd'] = talib.STOCH(high_price, low_price, close_price)
data['fastk'], data['fastd'] = talib.STOCHF(high_price, low_price,
close_price)
data['TRIX'] = talib.TRIX(close_price, timeperiod=max_time_window)
data['ULTOSC'] = talib.ULTOSC(high_price,
low_price,
close_price,
timeperiod1=max_time_window // 2,
timeperiod2=max_time_window,
timeperiod3=max_time_window * 2)
data['WILLR'] = talib.WILLR(high_price,
low_price,
close_price,
timeperiod=max_time_window)
data['NATR'] = talib.NATR(high_price,
low_price,
close_price,
timeperiod=max_time_window)
# data['_TRANGE'] = talib.TRANGE(high_price, low_price, close_price)
data = data.drop([open_name, close_name, high_name, low_name], axis=1)
# data.columns=data.columns.map(lambda x:x[1:])
data = data.dropna().astype(np.float32)
return data
def hsmadata_ta(self):
marketdata = pd.read_csv("raw data\\stock\\" + self.dataset + ".csv")
codelist = pd.Series(marketdata['code'].unique()).sort_values()
hsmadata = pd.DataFrame()
for code in codelist:
hsma0 = marketdata[marketdata['code'] == code].copy()
hsma0 = hsma0[hsma0['vol'] > 0].sort_values(by='date')
hsma0['ratio'] = hsma0['open'].shift(
-(self.day + 1)) / hsma0['open'].shift(-1) - 1
hsma0['ocratio'] = hsma0['open'].shift(-1) / hsma0['close'] - 1
hsma = hsma0[['date', 'code', 'ratio']].copy()
for l in self.length:
hsma['ROC_' + str(l)] = talib.ROC(
hsma0.close.values, timeperiod=l)
if l == 1:
hsma['freeturn_1'] = hsma0['free_turn']
else:
hsma['freeturn_' + str(l)] = talib.SUM(
hsma0.free_turn.values, timeperiod=l)
if l > 2:
hsma['CCI_' + str(l)] = talib.CCI(
hsma0.high.values,
hsma0.low.values,
hsma0.close.values,
timeperiod=l)
hsma['ADX_' + str(l)] = talib.ADX(
hsma0.high.values,
hsma0.low.values,
hsma0.close.values,
timeperiod=l)
hsma['MFI_' + str(l)] = talib.MFI(
hsma0.high.values,
hsma0.low.values,
hsma0.close.values,
hsma0.amt.values,
timeperiod=l)
hsma['RSI_' + str(l)] = talib.RSI(
hsma0.close.values, timeperiod=l)
hsma['VAR_' + str(l)] = talib.VAR(
hsma0.close.values, timeperiod=l)
hsma = hsma[hsma0['ocratio'] < 0.09]
hsmadata = pd.concat([hsmadata, hsma], ignore_index=True)
hsmadata = hsmadata.dropna()
hsmamean = pd.DataFrame()
for i in range(3, hsmadata.shape[1]):
temp = hsmadata.ix[:, [0, i]]
temp = temp.groupby(['date'],
as_index=False)[[temp.columns[1]]].mean()
temp = temp.sort_values(by='date')
temp = temp.rename(
columns={temp.columns[1]: (temp.columns[1] + '_mean')})
if hsmamean.shape[0] == 0:
hsmamean = temp
else:
hsmamean = pd.merge(hsmamean, temp)
hsmadata = pd.merge(hsmadata, hsmamean)
if not self.absratio:
temp = hsmadata[['date', 'ratio']]
ratio_mean = temp.groupby(['date'],
as_index=False)[['ratio']].mean()
ratio_mean = ratio_mean.sort_values(by='date')
ratio_mean = ratio_mean.rename(columns={'ratio': ('ratio_mean')})
hsmadata = pd.merge(hsmadata, ratio_mean)
hsmadata.ratio = hsmadata.ratio - hsmadata.ratio_mean
hsmadata = hsmadata.drop(['ratio_mean'], 1)
else:
ratio_mean = 0
return hsmadata, ratio_mean
def CCI(DataFrame, N=14):
res = talib.CCI(DataFrame.high.values, DataFrame.low.values,
DataFrame.close.values, N)
return pd.DataFrame({'CCI': res}, index=DataFrame.index)
def get_cci(df, timeperiod=14):
return talib.CCI(df.high, df.low, df.close, timeperiod=timeperiod)
